## 统计代写|统计推断代写Statistical inference代考|MAST90100

statistics-lab™ 为您的留学生涯保驾护航 在代写统计推断Statistical inference方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写统计推断Statistical inference代写方面经验极为丰富，各种代写统计推断Statistical inference相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 统计代写|统计推断代写Statistical inference代考|Signal Recovery Problem

One of the basic problems in Signal Processing is the problem of recovering a signal $x \in \mathbf{R}^{n}$ from noisy observations
$$y=A x+\eta$$
of a linear image of the signal under a given sensing mapping $x \mapsto A x: \mathbf{R}^{n} \rightarrow \mathbf{R}^{m}$; in (1.1), $\eta$ is the observation error. Matrix $A$ in (1.1) is called sensing matrix.
Recovery problems of the outlined types arise in many applications, including, but by far not reducing to,

• communications, where $x$ is the signal sent by the transmitter, $y$ is the signal recorded by the receiver, and $A$ represents the communication channel (reflecting, e.g., dependencies of decays in the signals’ amplitude on the transmitter-receiver distances); $\eta$ here typically is modeled as the standard (zero mean, unit covariance matrix) $m$-dimensional Gaussian noise; ${ }^{1}$
• image reconstruction, where the signal $x$ is an image – a $2 \mathrm{D}$ array in the usual photography, or a 3D array in tomography-and $y$ is data acquired by the imaging device. Here $\eta$ in many cases (although not always) can again be modeled as the standard Gaussian noise;
• linear regression, arising in a wide range of applications. In linear regression, one is given $m$ pairs “input $a^{i} \in \mathbf{R}^{n \text { ” }}$ to a “black box,” with output $y_{i} \in \mathbf{R}$. Sometimes we have reason to believe that the output is a corrupted by noise version of the “existing in nature,” but unobservable, “ideal output” $y_{i}^{*}=x^{T} a^{i}$ which is just a linear function of the input (this is called “linear regression model,” with inputs $a^{i}$ called “regressors”). Our goal is to convert actual observations $\left(a^{i}, y_{i}\right), 1 \leq i \leq m$, into estimates of the unknown “true” vector of parameters $x$. Denoting by $A$ the matrix with the rows $\left[a^{i}\right]^{T}$ and assembling individual observations $y_{i}$ into a single observation $y=\left[y_{1} ; \ldots ; y_{m}\right] \in \mathbf{R}^{m}$, we arrive at the problem of recovering vector $x$ from noisy observations of $A x$. Here again the most popular model for $\eta$ is the standard Gaussian noise.

## 统计代写|统计推断代写Statistical inference代考|Parametric and nonparametric cases

Recovering signal $x$ from observation $y$ would be easy if there were no observation noise $(\eta=0)$ and the rank of matrix $A$ were equal to the dimension $n$ of the signals. In this case, which arises only when $m \geq n$ (“more observations than unknown parameters”), and is typical in this range of $m$ and $n$, the desired $x$ would be the unique solution to the system of linear equations, and to find $x$ would be a simple problem of Linear Algebra. Aside from this trivial “enough observations, no noise” case, people over the years have looked at the following two versions of the recovery problem:

Parametric case: $m \gg n, \eta$ is nontrivial noise with zero mean, say, standard Gaussian. This is the classical statistical setup with the emphasis on how to use numerous available observations in order to suppress in the recovery, to the extent possible, the influence of observation noise.

Nonparametric case: $m \ll n .^{2}$ If addressed literally, this case seems to be senseless: when the number of observations is less that the number of unknown parameters, even in the noiseless case we arrive at the necessity to solve an undetermined (fewer equations than unknowns) system of linear equations. Linear Algebra says that if solvable, the system has infinitely many solutions. Moreover, the solution set (an affine subspace of positive dimension) is unbounded, meaning that the solutions are in no sense close to each other. A typical way to make the case of $m \ll n$ meaningful is to add to the observations (1.1) some a priori information about the signal. In traditional Nonparametric Statistics, this additional information is summarized in a bounded convex set $X \subset \mathbf{R}^{n}$, given to us in advance, known to contain the true signal $x$. This set usually is such that every signal $x \in X$ can be approximated by a linear combination of $s=1,2, \ldots, n$ vectors from a properly selected basis known to us in advance (“dictionary” in the slang of signal processing) within accuracy $\delta(s)$, where $\delta(s)$ is a function, known in advance, approaching 0 as $s \rightarrow \infty$. In this situation, with appropriate $A$ (e.g., just the unit matrix, as in the denoising problem), we can select some $s \leqslant m$ and try to recover $x$ as if it were a vector from the linear span $E_{s}$ of the first $s$ vectors of the outlined basis $[54,86,124,112,208]$. In the “ideal case,” $x \in E_{s}$, recovering $x$ in fact reduces to the case where the dimension of the signal is $s \ll m$ rather than $n \gg m$, and we arrive at the well-studied situation of recovering a signal of low (compared to the number of observations) dimension. In the “realistic case” of $x \delta(s)$-close to $E_{s}$, deviation of $x$ from $E_{s}$ results in an additional component in the recovery error (“bias”); a typical result of traditional Nonparametric Statistics quantifies the resulting error and minimizes it in $s[86,124,178,222,223,230,239]$. Of course, this outline of the traditional approach to “nonparametric” (with $n \gg m$ ) recovery problems is extremely sketchy, but it captures the most important fact in our context: with the traditional approach to nonparametric signal recovery, one assumes that after representing the signals by vectors of their coefficients in properly selected base, the $n$-dimensional signal to be recovered can be well approximated by an $s$-sparse (at most $s$ nonzero entries) signal, with $s \ll n$, and this sparse approximation can be obtained by zeroing out all but the first $s$ entries in the signal vector. The assumption just formulated indeed takes place for signals obtained by discretization of smooth uni- and multivariate functions, and this class of signals for several decades was the main, if not the only, focus of Nonparametric Statistics.

## 统计代写|统计推断代写Statistical inference代考|Compressed Sensing via ℓ1 minimization: Motivation

In principle there is nothing surprising in the fact that under reasonable assumption on the $m \times n$ sensing matrix $A$ we may hope to recover from noisy observations of $A x$ an $s$-sparse signal $x$, with $s \ll m$. Indeed, assume for the sake of simplicity that there are no observation errors, and let $\operatorname{Col}{j}[A]$ be $j$-th column in $A$. If we knew the locations $j{1}<j_{2}<\ldots<j_{s}$ of the nonzero entries in $x$, identifying $x$ could be reduced to solving the system of linear equations $\sum_{\ell=1}^{s} x_{i_{\ell}} \operatorname{Col}_{j \ell}[A]=y$ with $m$ equations and $s \ll m$ unknowns; assuming every $s$ columns in $A$ to be linearly independent (a quite unrestrictive assumption on a matrix with $m \geq s$ rows), the solution to the above system is unique, and is exactly the signal we are looking for. Of course, the assumption that we know the locations of nonzeros in $x$ makes the recovery problem completely trivial. However, it suggests the following course of action: given noiseless observation $y=A x$ of an s-sparse signal $x$, let us solve the combinatorial optimization problem
$$\min {z}\left{|z|{0}: A z=y\right},$$
where $|z|_{0}$ is the number of nonzero entries in $z$. Clearly, the problem has a solution with the value of the objective at most $s$. Moreover, it is immediately seen that if every $2 s$ columns in $A$ are linearly independent (which again is a very unrestrictive assumption on the matrix $A$ provided that $m \geq 2 s$ ), then the true signal $x$ is the unique optimal solution to $(1.2)$.
What was said so far can be extended to the case of noisy observations and “nearly $s$-sparse” signals $x$. For example, assuming that the observation error is “uncertainbut-bounded,” specifically some known norm $|\cdot|$ of this error does not exceed a given $\epsilon>0$, and that the true signal is s-sparse, we could solve the combinatorial optimization problem
$$\min {z}\left{|z|{0}:|A z-y| \leq \epsilon\right} .$$
Assuming that every $m \times 2 \mathrm{~s}$ submatrix $\bar{A}$ of $A$ is not just with linearly independent columns (i.e., with trivial kernel), but is reasonably well conditioned,
$$|\bar{A} w| \geq C^{-1}|w|_{2}$$
for all ( $2 s)$-dimensional vectors $w$, with some constant $C$, it is immediately seen that the true signal $x$ underlying the observation and the optimal solution $\widehat{x}$ of (1.3) are close to each other within accuracy of order of $\epsilon:|x-\widehat{x}|_{2} \leq 2 C \epsilon$. It is easily seen that the resulting error bound is basically as good as it could be.

## 统计代写|统计推断代写Statistical inference代考|Signal Recovery Problem

• 通讯，在哪里X是发射机发送的信号，是是接收器记录的信号，并且一个表示通信信道（反映，例如，信号幅度衰减对发射机-接收机距离的依赖性）；这这里通常被建模为标准（零均值，单位协方差矩阵）米-维高斯噪声；1
• 图像重建，其中信号X是一个图像——一个2D通常摄影中的阵列，或断层扫描中的 3D 阵列 – 和是是成像设备获取的数据。这里这在许多情况下（尽管并非总是如此）可以再次建模为标准高斯噪声；
• 线性回归，在广泛的应用中出现。在线性回归中，给出一个米对“输入一个一世∈Rn ” 到一个“黑匣子”，输出是一世∈R. 有时我们有理由相信输出是“存在于自然界”但不可观察的“理想输出”的噪声版本是一世∗=X吨一个一世这只是输入的线性函数（这称为“线性回归模型”，输入一个一世称为“回归器”）。我们的目标是转换实际观察结果(一个一世,是一世),1≤一世≤米, 估计未知的“真实”参数向量X. 表示一个具有行的矩阵[一个一世]吨并收集个人观察结果是一世一次观察是=[是1;…;是米]∈R米，我们得到了恢复向量的问题X从嘈杂的观察一个X. 这里又是最受欢迎的模型这是标准高斯噪声。

## 统计代写|统计推断代写Statistical inference代考|Compressed Sensing via ℓ1 minimization: Motivation

\min {z}\left{|z|{0}: A z=y\right},\min {z}\left{|z|{0}: A z=y\right},

\min {z}\left{|z|{0}:|A zy| \leq \epsilon\right} 。\min {z}\left{|z|{0}:|A zy| \leq \epsilon\right} 。

|一个¯在|≥C−1|在|2

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

## 数学代写|黎曼几何代写Riemannian geometry代考|МАТН6205

statistics-lab™ 为您的留学生涯保驾护航 在代写黎曼几何Riemannian geometry方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写黎曼几何Riemannian geometry代写方面经验极为丰富，各种代写黎曼几何Riemannian geometry相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 数学代写|黎曼几何代写Riemannian geometry代考|The First Dirichlet Eigenvalue Comparison Theorem

Following standard notations and setting (see, e.g., [Cha1] or in this context the seminal survey by Grigoryan in [Gri1]), for any precompact open set $\Omega$ in a Riemannian manifold $M$ we denote by $\lambda(\Omega)$ the smallest number $\lambda$ for which the following Dirichlet eigenvalue problem has a non-zero solution
\left{\begin{aligned} \Delta u+\lambda u &=0 \text { at all points } x \text { in } \Omega \ u(x) &=0 \text { at all points } x \text { in } \partial \Omega \end{aligned}\right.
We shall need the following beautiful observation due to Barta:

Theorem $7.1$ ([B], [Cha1]). Consider any smooth function $f$ on a domain $\Omega$ which satisfies $f_{\left.\right|{\Omega}}>0$ and $f{\mid \text {an }}=0$, and let $\lambda(\Omega)$ denote the first eigenvalue of the Dirichlet problem for $\Omega$. Then
$$\inf {\Omega}\left(\frac{\Delta f}{f}\right) \leq-\lambda(\Omega) \leq \sup {\Omega}\left(\frac{\Delta f}{f}\right)$$
If equality occurs in one of the inequalities, then they are both equalities, and $f$ is an eigenfunction for $\Omega$ corresponding to the eigenvalue $\lambda(\Omega)$.
Proof. Let $\phi$ be an eigenfunction for $\Omega$ corresponding to $\lambda(\Omega)$.
Then $\phi_{\Omega}>0$ and $\phi_{\left.\right|{\Omega}}=0$. If we let $h$ denote the difference $h=\phi-f$, then \begin{aligned} -\lambda(\Omega)=\frac{\Delta \phi}{\phi} &=\frac{\Delta f}{f}+\frac{f \Delta h-h \Delta f}{f(f+h)} \ &=\inf {\Omega}\left(\frac{\Delta f}{f}\right)+\sup {\Omega}\left(\frac{f \Delta h-h \Delta f}{f(f+h)}\right) \ &=\sup {\Omega}\left(\frac{\Delta f}{f}\right)+\inf {\Omega}\left(\frac{f \Delta h-h \Delta f}{f(f+h)}\right) \end{aligned} Here the supremum, $\sup {\Omega}\left(\frac{f \Delta h-h \Delta f}{f(f+h)}\right)$ is necessarily positive since
$$\left.f(f+h)\right|{\Omega}>0$$ and since by Green’s second formula $(6.8)$ in Theorem $6.4$ we have $$\int{\Omega}(f \Delta h-h \Delta f) d V=0 \text {. }$$
For the same reason, the infimum, $\inf _{\Omega}\left(\frac{f \Delta h-h \Delta f}{f(f+h)}\right)$ is necessarily negative. This gives the first part of the theorem. If equality occurs, then $(f \Delta h-h \Delta f)$ must vanish identically on $\Omega$, so that $-\lambda(\Omega)=\frac{\Delta f}{f}$, which gives the last part of the statement.

As already alluded to in the introduction, the key heuristic message of this report is that the Laplacian is a particularly ‘swift actor’ on minimal submanifolds (i.e., minimal extrinsic regular $R$-balls $D_{R}$ ) in ambient spaces with an upper bound $b$ on its sectional curvatures. This is to be understood in comparison with the ‘action’ of the Laplacian on totally geodesic $R$-balls $B_{R}^{b, m}$ in spaces of constant curvature b. In this section we will use Barta’s theorem to show that this phenomenon can indeed be ‘heard’ by ‘listening’ to the bass note of the Dirichlet spectrum of any given $D_{R}$.

## 数学代写|黎曼几何代写Riemannian geometry代考|Isoperimetric Relations

In this and the following two sections we survey some comparison results concerning inequalities of isoperimetric type, mean exit times and capacities, respectively, for extrinsic minimal balls in ambient spaces with an upper bound on sectional curvature. This has been developed in a series of papers, see [Pa] and [MaP1][MaP4].

We will still assume a standard situation as in the previous section, i.e., $D_{R}$ denotes an extrinsic minimal ball of a minimal submanifold $P$ in an ambient space $N$ with the upper bound $b$ on the sectional curvatures.

Proposition 8.1. We define the following function of $t \in \mathbb{R}{+} \cup{0}$ for every $b \in \mathbb{R}$, for every $q \in \mathbb{R}$, and for every dimension $m \geq 2$ : $$L{q}^{b, m}(t)=q\left(\frac{\operatorname{Vol}\left(S_{t}^{b, m-1}\right)}{m h_{b}(t)}-\operatorname{Vol}\left(B_{t}^{b, m}\right)\right)$$
Then
$$L_{q}^{b, m}(0)=0 \text { for all } b, q, \text { and } m$$
and
$$\operatorname{sign}\left(\frac{d}{d t} L_{q}^{b, m}(t)\right)=\operatorname{sign}(b q) \text { for all } b, q, m, \text { and } t>0 \text {. }$$
Proof. This follows from a direct computation using the definition of $h_{b}(t)$ from equation (3.5) together with the volume formulae (cf. [Gr])
\begin{aligned} \operatorname{Vol}\left(B_{t}^{b, m}\right) &=\operatorname{Vol}\left(S_{1}^{0, m-1}\right) \cdot \int_{0}^{t}\left(Q_{b}(u)\right)^{m-1} d u \ \operatorname{Vol}\left(S_{t}^{b, m-1}\right) &=\operatorname{Vol}\left(S_{1}^{0, m-1}\right) \cdot\left(Q_{b}(t)\right)^{m-1} \end{aligned}

## 数学代写|黎曼几何代写Riemannian geometry代考|A Consequence of the Co-area Formula

The co-area equation (6.4) applied to our setting gives the following
Proposition 9.1. Let $D_{R}(p)$ denote a regular extrinsic minimal ball of $P$ with center $p$ in $N$. Then
$$\frac{d}{d u} \operatorname{Vol}\left(D_{u}\right) \geq \operatorname{Vol}\left(\partial D_{u}\right) \text { for all } u \leq R$$

Proof. We let $f: \bar{D}{R} \rightarrow \mathbb{R}$ denote the function $f(x)=R-r(x)$, which clearly vanishes on the boundary of $D{R}$ and is smooth except at $p$. Following the notation of the co-area formula we further let
\begin{aligned} \Omega(t) &=D_{(R-t)} \ V(t) &=\operatorname{Vol}\left(D_{(R-t)}\right) \text { and } \ \Sigma(t) &=\partial D_{(R-t)} \end{aligned}
Then
\begin{aligned} \operatorname{Vol}\left(D_{u}\right) &=V(R-u) \text { so that } \ \frac{d}{d u} \operatorname{Vol}\left(D_{u}\right) &=-V^{\prime}(t){\left.\right|{i=n-u}} . \end{aligned}
The co-area equation (6.4) now gives
\begin{aligned} -V^{\prime}(t) &=\int_{\partial D_{(R-t)}}\left|\nabla^{P} r\right|^{-1} d A \ & \geq \operatorname{Vol}\left(\partial D_{(R-t)}\right) \ &=\operatorname{Vol}\left(\partial D_{u}\right) \end{aligned}
and this proves the statement.
Exercise 9.2. Explain why the non-smoothness of the function $f$ at $p$ does not create problems for the application of equation (6.4) in this proof although smoothness is one of the assumptions in Theorem 6.1.

## 数学代写黎曼几何代写Riemannian geometry代 考|lsoperimetric Relations

$$L q^{b, m}(t)=q\left(\frac{\operatorname{Vol}\left(S_{t}^{b, m-1}\right)}{m h_{b}(t)}-\operatorname{Vol}\left(B_{t}^{b, m}\right)\right)$$

$$L_{q}^{b, m}(0)=0 \text { for all } b, q, \text { and } m$$

$$\operatorname{sign}\left(\frac{d}{d t} L_{q}^{b, m}(t)\right)=\operatorname{sign}(b q) \text { for all } b, q, m, \text { and } t>0$$

$$\operatorname{Vol}\left(B_{t}^{b, m}\right)=\operatorname{Vol}\left(S_{1}^{0, m-1}\right) \cdot \int_{0}^{t}\left(Q_{b}(u)\right)^{m-1} d u \operatorname{Vol}\left(S_{t}^{b, m-1}\right)=\operatorname{Vol}\left(S_{1}^{0, m-1}\right) \cdot\left(Q_{b}(t)\right)^{m-1}$$

## 数学代写黎曼几何代写Riemannian geometry代考|A Consequence of the Co-area Formula

$$\frac{d}{d u} \operatorname{Vol}\left(D_{u}\right) \geq \operatorname{Vol}\left(\partial D_{u}\right) \text { for all } u \leq R$$

$$\Omega(t)=D_{(R-t)} V(t)=\operatorname{Vol}\left(D_{(R-t)}\right) \text { and } \Sigma(t)=\partial D_{(R-t)}$$

$$\operatorname{Vol}\left(D_{u}\right)=V(R-u) \text { so that } \frac{d}{d u} \operatorname{Vol}\left(D_{u}\right) \quad=-V^{\prime}(t) \mid i=n-u$$

$$-V^{\prime}(t)=\int_{\partial D_{(R-t)}}\left|\nabla^{P} r\right|^{-1} d A \geq \operatorname{Vol}\left(\partial D_{(R-t)}\right)=\operatorname{Vol}\left(\partial D_{u}\right)$$

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

## 数学代写|黎曼几何代写Riemannian geometry代考|MATH3342

statistics-lab™ 为您的留学生涯保驾护航 在代写黎曼几何Riemannian geometry方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写黎曼几何Riemannian geometry代写方面经验极为丰富，各种代写黎曼几何Riemannian geometry相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 数学代写|黎曼几何代写Riemannian geometry代考|Analysis of Lorentzian Distance Functions

For comparison, and before going further into the Riemannian setting, we briefly present the corresponding Hessian analysis of the distance function from a point in a Lorentzian manifold and its restriction to a spacelike hypersurface. The results can be found in [AHP], where the corresponding Hessian analysis was also carried out, i.e., the analysis of the Lorentzian distance from an achronal spacelike hypersurface in the style of Proposition 3.9. Recall that in Section 3 we also considered

the analysis of the distance from a totally geodesic hypersurface $P$ in the ambient Riemannian manifold $N$.

Let $\left(N^{n+1}, g\right)$ denote an $(n+1)$-dimensional spacetime, that is, a timeoriented Lorentzian manifold of dimension $n+1 \geq 2$. The metric tensor $g$ has index 1 in this case, and, as we did in the Riemannian context, we shall denote it alternatively as $g=\langle,$,$rangle (see, e.g., [O’N] as a standard reference for this section).$
Given $p, q$ two points in $N$, one says that $q$ is in the chronological future of $p$, written $p \ll q$, if there exists a future-directed timelike curve from $p$ to $q$. Similarly, $q$ is in the causal future of $p$, written $p<q$, if there exists a future-directed causal (i.e., nonspacelike) curve from $p$ to $q$.
Then the chronological future $I^{+}(p)$ of a point $p \in N$ is defined as
$$I^{+}(p)={q \in N: p \ll q} .$$
The Lorentzian distance function $d: N \times N \rightarrow[0,+\infty]$ for an arbitrary spacetime may fail to be continuous in general, and may also fail to be finite-valued. But there are geometric restrictions that guarantee a good behavior of $d$. For example, globally hyperbolic spacetimes turn out to be the natural class of spacetimes for which the Lorentzian distance function is finite-valued and continuous.

Given a point $p \in N$, one can define the Lorentzian distance function $d_{p}$ :
$M \rightarrow[0,+\infty]$ with respect to $p$ by
$$d_{p}(q)=d(p, q) .$$
In order to guarantee the smoothness of $d_{p}$, we need to restrict this function on certain special subsets of $N$. Let $\left.T_{-1} N\right|{p}$ be the following set $$\left.T{-1} N\right|{p}=\left{v \in T{p} N: v \text { is a future-directed timelike unit vector }\right} .$$
Define the function $s_{p}:\left.T_{-1} N\right|{p} \rightarrow[0,+\infty]$ by $$s{p}(v)=\sup \left{t \geq 0: d_{p}\left(\gamma_{v}(t)\right)=t\right},$$
where $\gamma_{v}:[0, a) \rightarrow N$ is the future inextendible geodesic starting at $p$ with initial velocity $v$. Then we define
$$\tilde{\mathcal{I}}^{+}(p)=\left{t v: \text { for all }\left.v \in T_{-1} N\right|{p} \text { and } 0{p}(v)\right}$$
and consider the subset $\mathcal{I}^{+}(p) \subset N$ given by
$$\mathcal{I}^{+}(p)=\exp {p}\left(\operatorname{int}\left(\tilde{\mathcal{I}}^{+}(p)\right)\right) \subset I^{+}(p) .$$ Observe that the exponential map $$\exp {p}: \operatorname{int}\left(\tilde{\mathcal{I}}^{+}(p)\right) \rightarrow \mathcal{I}^{+}(p)$$
is a diffeomorphism and $\mathcal{I}^{+}(p)$ is an open subset (possible empty).
Remark 4.1. When $b \geq 0$, the Lorentzian space form of constant sectional curvature $b$, which we denote as $N_{b}^{n+1}$, is globally hyperbolic and geodesically complete, and every future directed timelike unit geodesic $\gamma_{b}$ in $N_{b}^{n+1}$ realizes the Lorentzian distance between its points. In particular, if $b \geq 0$ then $\mathcal{I}^{+}(p)=I^{+}(p)$ for every point $p \in N_{b}^{n+1}$ (see [EGK, Remark 3.2]).

## 数学代写|黎曼几何代写Riemannian geometry代考|Concerning the Riemannian Setting and Notation

Returning now to the Riemannian case: Although we indeed do have the possibility of considering 4 basically different settings determined by the choice of $p$ or $V$ as the ‘base’ of our normal domain and the choice of $K_{N} \leq b$ or $K_{N} \geq b$ as the curvature assumption for the ambient space $N$, we will, however, mainly consider the ‘first’ of these. Specifically we will (unless otherwise explicitly stated) apply the following assumptions and denotations:
Definition 5.1. A standard situation encompasses the following:
(1) $P^{m}$ denotes an $m$-dimensional complete minimally immersed submanifold of the Riemannian manifold $N^{n}$. We always assume that $P$ has dimension $m \geq 2 .$
(2) The sectional curvatures of $N$ are assumed to satisfy $K_{N} \leq b, b \in \mathbb{R}$, cf. Proposition $3.10$, equation (3.13).
(3) The intersection of $P$ with a regular ball $B_{R}(p)$ centered at $p \in P$ (cf. Definition 3.4) is denoted by
$$D_{R}=D_{R}(p)=P^{m} \cap B_{R}(p)$$
and this is called a minimal extrinsic $R$-ball of $P$ in $N$, see the Figures 3-7 of extrinsic balls, which are cut out from some of the well-known minimal surfaces in $\mathbb{R}^{3}$.
(4) The totally geodesic $m$-dimensional regular $R$-ball centered at $\tilde{p}$ in $\mathbb{K}^{n}(b)$ is denoted by
$$B_{R}^{b, m}=B_{R}^{b, m}(\tilde{p})$$
whose boundary is the $(m-1)$-dimensional sphere
$$\partial B_{R}^{b, m}=S_{R}^{b, m-1}$$
(5) For any given smooth function $F$ of one real variable we denote
$$W_{F}(r)=F^{\prime \prime}(r)-F^{\prime}(r) h_{b}(r) \text { for } 0 \leq r \leq R$$
We may now collect the basic inequalities from our previous analysis as follows.

## 数学代写|黎曼几何代写Riemannian geometry代考|Green’s Formulae and the Co-area Formula

Now we recall the coarea formula. We follow the lines of [Sa] Chapter II, Section 5. Let $(M, g)$ denote a Riemannian manifold and $\Omega$ a precompact domain in $M$. Let $\psi: \Omega \rightarrow \mathbb{R}$ be a smooth function such that $\psi(\Omega)=[a, b]$ with $a<b$. Denote by $\Omega_{0}$ the set of critical points of $\psi$. By Sard’s theorem, the set of critical values $S_{\psi}=\psi\left(\Omega_{0}\right)$ has null measure, and the set of regular values $R_{\psi}=[a, b]-S_{\psi}$ is open. In particular, for any $t \in R_{\psi}=[a, b]-S_{\psi}$, the set $\Gamma(t):=\psi^{-1}(t)$ is a smooth embedded hypersurface in $\Omega$ with $\partial \Gamma(t)=\emptyset$. Since $\Gamma(t) \subseteq \Omega-\Omega_{0}$ then $\nabla \psi$ does not vanish along $\Gamma(t)$; indeed, a unit normal along $\Gamma(t)$ is given by $\nabla \psi /|\nabla \psi|$.
Now we let
\begin{aligned} &A(t)=\operatorname{Vol}(\Gamma(t)) \ &\Omega(t)={x \in \bar{\Omega} \mid \psi(x)<t} \ &V(t)=\operatorname{Vol}(\Omega(t)) \end{aligned}
Theorem 6.1.
i) For every integrable function $u$ on $\bar{\Omega}$ :
$$\int_{\Omega} u \cdot|\nabla \psi| d V=\int_{a}^{b}\left(\int_{\Gamma(t)} u d A_{t}\right) d t,$$
where $d A_{t}$ is the Riemannian volume element defined from the induced metric $g_{t}$ on $\Gamma(t)$ from $g$.
ii) The function $V(t)$ is a smooth function on the regular values of $\psi$ given by:
$$V(t)=\operatorname{Vol}\left(\Omega_{0} \cap \Omega(t)\right)+\int_{a}^{t}\left(\int_{\Gamma(t)}|\nabla \psi|^{-1} d A_{t}\right)$$
and its derivative is
$$\frac{d}{d t} V(t)=\int_{\Gamma(t)}|\nabla \psi|^{-1} d A_{t}$$

## 数学代写|黎曼几何代写Riemannian geometry代考|Analysis of Lorentzian Distance Functions

dp(q)=d(p,q).

\left.T{-1} N\right|{p}=\left{v \in T{p} N: v \text { 是一个面向未来的类时单位向量 }\right} 。\left.T{-1} N\right|{p}=\left{v \in T{p} N: v \text { 是一个面向未来的类时单位向量 }\right} 。

s{p}(v)=\sup \left{t \geq 0: d_{p}\left(\gamma_{v}(t)\right)=t\right},s{p}(v)=\sup \left{t \geq 0: d_{p}\left(\gamma_{v}(t)\right)=t\right},

\tilde{\mathcal{I}}^{+}(p)=\left{t v: \text { for all }\left.v \in T_{-1} N\right|{p} \text { 和} 0{p}(v)\right}\tilde{\mathcal{I}}^{+}(p)=\left{t v: \text { for all }\left.v \in T_{-1} N\right|{p} \text { 和} 0{p}(v)\right}

## 数学代写|黎曼几何代写Riemannian geometry代考|Concerning the Riemannian Setting and Notation

(1)磷米表示一个米黎曼流形的一维完全最小浸没子流形ñn. 我们总是假设磷有维度米≥2.
(2) 截面曲率ñ假设满足ķñ≤b,b∈R，参见。主张3.10，等式（3.13）。
(3) 交集磷用普通球乙R(p)以p∈磷（参见定义 3.4）表示为

DR=DR(p)=磷米∩乙R(p)

(4) 完全测地线米维规则R- 球为中心p~在ķn(b)表示为

∂乙Rb,米=小号Rb,米−1
(5) 对于任何给定的平滑函数F我们表示的一个实变量

## 数学代写|黎曼几何代写Riemannian geometry代考|Green’s Formulae and the Co-area Formula

i) 对于每个可积函数在上Ω¯ :

∫Ω在⋅|∇ψ|d在=∫一个b(∫Γ(吨)在d一个吨)d吨,

ii) 功能在(吨)是一个关于正则值的平滑函数ψ给出：

dd吨在(吨)=∫Γ(吨)|∇ψ|−1d一个吨

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

## 数学代写|黎曼几何代写Riemannian geometry代考|MATH3405

statistics-lab™ 为您的留学生涯保驾护航 在代写黎曼几何Riemannian geometry方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写黎曼几何Riemannian geometry代写方面经验极为丰富，各种代写黎曼几何Riemannian geometry相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 数学代写|黎曼几何代写Riemannian geometry代考|Appetizer and Introduction

It is a natural and indeed a classical question to ask: “What is the effective resistance of, say, a hyperboloid or a helicoid if the surface is made of a homogeneous conducting material?”.

In these notes we will study the precise meaning of this and several other related questions and analyze how the answers depend on the curvature and topology of the given surfaces and manifolds. We will focus mainly on minimal submanifolds in ambient spaces which are assumed to have a well-defined upper (or lower) bound on their sectional curvatures.

One key ingredient is the comparison theory for distance functions in such spaces. In particular we establish and use a comparison result for the Laplacian of geometrically restricted distance functions. It is in this setting that we obtain information about such diverse phenomena as diffusion processes, isoperimetric inequalities, Dirichlet eigenvalues, transience, recurrence, and effective resistance of the spaces in question. In this second edition of the present notes we extend those previous findings in four ways: Firstly, we include comparison results for the exit time moment spectrum for compact domains in Riemannian manifolds; Secondly, and most substantially, we report on very recent results obtained by the first and third author together with C. Rosales concerning comparison results for the capacities and the type problem (transient versus recurrent) in weighted Riemannian manifolds; Thirdly we survey how some of the purely Riemannian results on transience and recurrence can be lifted to the setting of spacelike submanifolds in Lorentzian manifolds; Fourthly, the comparison spaces that we employ for some of the new results are typically so-called model spaces, i.e., warped products (gen= eralized surfaces of revolution) where ‘all the geometry’ in each case is determined by a given radial warping function and a given weight function.In a sense, all the different phenomena that we consider are ‘driven’ by the Laplace operator which in turn depends on the background curvatures and the weight function. One key message of this report is that the Laplacian is a particularly ‘swift’ operator – for example on minimal submanifolds in ambient spaces with small sectional curvatures – but depending on the weight functions. Specifically, we observe and report new findings about this behaviour in the contexts of both Riemannian, Lorentzian, and weighted geometries, see Sections 12 and $20-27$. Similar results generally hold true within the intrinsic geometry of the manifolds themselves – often even with Ricci curvature lower bounds (see, e.g., the survey [Zhu]) as a substitute for the specific assumption of a lower bound on sectional curvatures.

## 数学代写|黎曼几何代写Riemannian geometry代考|The Comparison Setting and Preliminaries

We consider a complete immersed submanifold $P^{m}$ in a Riemannian manifold $N^{n}$, and denote by $\mathrm{D}^{P}$ and $\mathrm{D}^{N}$ the Riemannian connections of $P$ and $N$, respectively. We refer to the excellent general monographs on Riemannian geometry – e.g., [Sa], [CheeE], and [Cha2] – for the basic notions, that will be applied in these notes. In particular we shall be concerned with the second-order behavior of certain functions on $P$ which are obtained by restriction from the ambient space $N$ as displayed in Proposition $3.1$ below. The second-order derivatives are defined in terms of the Hessian operators Hess ${ }^{N}$, Hess ${ }^{P}$ and their traces $\Delta^{N}$ and $\Delta^{P}$, respectively (see, e.g., [Sa] p. 31). The difference between these operators quite naturally involves geometric second-order information about how $P^{m}$ actually sits inside $N^{n}$. This information is provided by the second fundamental form $\alpha$ (resp. the mean curvature $H$ ) of $P$ in $N$ (see [Sa] p. 47). If the functions under consideration are essentially distance functions in $N$ – or suitably modified distance functions then their second-order behavior is strongly influenced by the curvatures of $N$, as is directly expressed by the second variation formula for geodesics ([Sa] p. 90).

As is well known, the ensuing and by now classical comparison theorems for Jacobi fields give rise to the celebrated Toponogov theorems for geodesic triangles and to powerful results concerning the global structure of Riemannian spaces ([Sa], Chapters IV-V). In these notes, however, we shall mainly apply the Jacobi field comparison theory only off the cut loci of the ambient space $N$, or more precisely, within the regular balls of $N$ as defined in Definition $3.4$ below. On the other hand, from the point of view of a given (minimal) submanifold $P$ in $N$, our results for $P$ are semi-global in the sense that they apply to domains which are not necessarily distance-regular within $P$.

## 数学代写|黎曼几何代写Riemannian geometry代考|Analysis of Riemannian Distance Functions

Let $\mu: N \mapsto \mathbb{R}$ denote a smooth function on $N$. Then the restriction $\tilde{\mu}=\mu_{\left.\right|{P}}$ is a smooth function on $P$ and the respective Hessians $\operatorname{Hess}^{N}(\mu)$ and $\operatorname{Hess}^{P}(\tilde{\mu})$ are related as follows: Proposition $3.1([\mathrm{JK}]$ p. 713$)$. \begin{aligned} \operatorname{Hess}^{P}(\tilde{\mu})(X, Y)=& \operatorname{Hess}^{N}(\mu)(X, Y) \ &+\left\langle\nabla^{N}(\mu), \alpha(X, Y)\right\rangle \end{aligned} for all tangent vectors $X, Y \in T P \subseteq T N$, where $\alpha$ is the second fundamental form of $P$ in $N$. Proof. \begin{aligned} \operatorname{Hess}^{P}(\tilde{\mu})(X, Y) &=\left\langle\mathrm{D}{X}^{P} \nabla^{P} \tilde{\mu}, Y\right\rangle \ &=\left\langle\mathrm{D}{X}^{N} \nabla^{P} \tilde{\mu}-\alpha\left(X, \nabla^{P} \tilde{\mu}\right), Y\right\rangle \ &=\left\langle\mathrm{D}{X}^{N} \nabla^{P} \tilde{\mu}, Y\right\rangle \ &=X\left(\left\langle\nabla^{P} \tilde{\mu}, Y\right\rangle\right)-\left\langle\nabla^{P} \tilde{\mu}, \mathrm{D}{X}^{N} Y\right\rangle \ &=\left\langle\mathrm{D}{X}^{N} \nabla^{N} \mu, Y\right\rangle+\left\langle\left(\nabla^{N} \mu\right)^{\perp}, \mathrm{D}_{X}^{N} Y\right\rangle \ &=\operatorname{Hess}^{N}(\mu)(X, Y)+\left\langle\left(\nabla^{N} \mu\right)^{\perp}, \alpha(X, Y)\right\rangle \ &=\operatorname{Hess}^{N}(\mu)(X, Y)+\left\langle\nabla^{N} \mu, \alpha(X, Y)\right\rangle \end{aligned}
If we modify $\mu$ to $F \circ \mu$ by a smooth function $F: \mathbb{R} \mapsto \mathbb{R}$, then we get
Lemma 3.2.
\begin{aligned} \operatorname{Hess}^{N}(F \circ \mu)(X, X)=& F^{\prime \prime}(\mu) \cdot\left\langle\nabla^{N}(\mu), X\right\rangle^{2} \ &+F^{\prime}(\mu) \cdot \operatorname{Hess}^{N}(\mu)(X, X) \end{aligned}
for all $X \in T N^{n}$

In the following we write $\mu=\tilde{\mu}$. Combining (3.1) and (3.3) then gives
Corollary 3.3.
\begin{aligned} \operatorname{Hess}^{P}(F \circ \mu)(X, X)=& F^{\prime \prime}(\mu) \cdot\left\langle\nabla^{N}(\mu), X\right\rangle^{2} \ &+F^{\prime}(\mu) \cdot \operatorname{Hess}^{N}(\mu)(X, X) \ &+\left\langle\nabla^{N}(\mu), \alpha(X, X)\right\rangle \end{aligned}
for all $X \in T P^{m}$.
In what follows the function $\mu$ will always be a distance function in $N$-either from a point $p$ in which case we set $\mu(x)=\operatorname{dist}{N}(p, x)=r(x)$, or from a totally geodesic hypersurface $V^{n-1}$ in $N$ in which case we let $\mu(x)=$ dist ${N}(V, x)=$ $\eta(x)$. The function $F$ will always be chosen, so that $F \circ \mu$ is smooth inside the respective regular balls around $p$ and inside the regular tubes around $V$, which we now define. The sectional curvatures of the two-planes $\Omega$ in the tangent bundle of the ambient space $N$ are denoted by $K_{N}(\Omega)$, see, e.g., [Sa], Section II.3. Concerning the notation: In the following both Hess $^{N}$ and Hess will be used invariantly for both the Hessian in the ambient manifold $N$, as well as in a purely intrinsic context where only $N$ and not any of its submanifolds is under consideration.

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

## 物理代写|量子场论代写Quantum field theory代考|PHYS8302

statistics-lab™ 为您的留学生涯保驾护航 在代写量子场论Quantum field theory方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写量子场论Quantum field theory代写方面经验极为丰富，各种代写量子场论Quantum field theory相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 物理代写|量子场论代写Quantum field theory代考|Thermal Correlation Functions

The energies of excited states are encoded in the thermal correlation functions. These functions are expectation values of products of the position operator
$$\hat{q}{\mathrm{E}}(\tau)=\mathrm{e}^{\tau \hat{H} / \hbar} \hat{q} \mathrm{e}^{-\tau \hat{H} / \hbar}, \quad \hat{q}{\mathrm{E}}(0)=\hat{q}(0),$$
at different imaginary times in the canonical ensemble,
$$\left\langle\hat{q}{\mathrm{E}}\left(\tau{1}\right) \cdots \hat{q}{\mathrm{E}}\left(\tau{n}\right)\right\rangle_{\beta} \equiv \frac{1}{Z(\beta)} \operatorname{tr}\left(\mathrm{e}^{-\beta \hat{H}} \hat{q}{\mathrm{E}}\left(\tau{1}\right) \cdots \hat{q}{\mathrm{E}}\left(\tau{n}\right)\right)$$
The normalizing function $Z(\beta)$ is the partition function (2.56). From the thermal two-point function
\begin{aligned} \left\langle\hat{q}{\mathrm{E}}\left(\tau{1}\right) \hat{q}{\mathrm{E}}\left(\tau{2}\right)\right\rangle_{\beta} &=\frac{1}{Z(\beta)} \operatorname{tr}\left(\mathrm{e}^{-\beta \hat{H}} \hat{q}{\mathrm{E}}\left(\tau{1}\right) \hat{q}{\mathrm{E}}\left(\tau{2}\right)\right) \ &=\frac{1}{Z(\beta)} \operatorname{tr}\left(\mathrm{e}^{-\left(\beta-\tau_{1}\right) \hat{H}} \hat{q} \mathrm{e}^{-\left(\tau_{1}-\tau_{2}\right) \hat{H}} \hat{q} \mathrm{e}^{-\tau_{2} \hat{H}}\right) \end{aligned}
we can extract the energy gap between the ground state and the first excited state. For this purpose we use orthonormal energy eigenstates $|n\rangle$ to calculate the trace and in addition insert the resolution of the identity operator $\mathbb{1}=\sum|m\rangle\langle m|$. This yields
$$\langle\ldots\rangle_{\beta}=\frac{1}{Z(\beta)} \sum_{n, m} \mathrm{e}^{-\left(\beta-\tau_{1}+\tau_{2}\right) E_{n}} \mathrm{e}^{-\left(\tau_{1}-\tau_{2}\right) E_{\mathrm{m}}}\langle n|\hat{q}| m\rangle\langle m|\hat{q}| n\rangle$$
Note that in the sum over $n$ the contributions from the excited states are exponentially suppressed at low temperatures $\beta \rightarrow \infty$, implying that the thermal two-point function converges to the Schwinger function in this limit:
$$\left\langle\hat{q}{\mathrm{E}}\left(\tau{1}\right) \hat{q}{\mathrm{E}}\left(\tau{2}\right)\right\rangle_{\beta} \stackrel{\beta \rightarrow \infty}{\longrightarrow} \sum_{m>0} \mathrm{e}^{-\left(\tau_{1}-\tau_{2}\right)\left(E_{m}-E_{0}\right)}|\langle 0|\hat{q}| m\rangle|^{2}=\left\langle 0\left|\hat{q}{\mathrm{E}}\left(\tau{1}\right) \hat{q}{\mathrm{E}}\left(\tau{2}\right)\right| 0\right\rangle$$

## 物理代写|量子场论代写Quantum field theory代考|The Harmonic Oscillator

We wish to study the path integral for the Euclidean oscillator with discretized time. The oscillator is one of the few systems for which the path integral can be calculated explicitly. For more such system, the reader may consult the text [19]. But the results for the oscillator are particularly instructive with regard to lattice field theories considered later in this book. So let us discretize the Euclidean time interval $[0, \tau]$ with $n$ sampling points separated by a lattice constant $\varepsilon=\tau / n$. For the Lagrangian
$$L=\frac{m}{2} \dot{q}^{2}+\mu q^{2}$$
the discretized path integral over periodic paths reads
\begin{aligned} Z &=\int \mathrm{d} q_{1} \cdots \mathrm{d} q_{n}\left(\frac{m}{2 \pi \varepsilon}\right)^{n / 2} \exp \left{-\varepsilon \sum_{j=0}^{n-1}\left(\frac{m}{2}\left(\frac{q_{j+1}-q_{j}}{\varepsilon}\right)^{2}+\mu q_{j}^{2}\right)\right} \ &=\left(\frac{m}{2 \pi \varepsilon}\right)^{n / 2} \int \mathrm{d} q_{1} \cdots \mathrm{d} q_{n} \exp \left(-\frac{1}{2}(\boldsymbol{q}, \mathrm{A} q)\right) \end{aligned}
where we assumed $q_{0}=q_{n}$ and introduced the symmetric matrix
$$\mathrm{A}=\frac{m}{\varepsilon}\left(\begin{array}{cccccc} \alpha & -1 & 0 & \cdots & 0 & -1 \ -1 & \alpha & -1 & \cdots & 0 & 0 \ & & \ddots & & & \ & & & \ddots & & \ 0 & 0 & \cdots & -1 & \alpha & -1 \ -1 & 0 & \cdots & 0 & -1 & \alpha \end{array}\right), \quad \alpha=2\left(1+\frac{\mu}{m} \varepsilon^{2}\right)$$
This is a Toeplitz matrix in which each descending diagonal from left to right is constant. This property results from the invariance of the action under lattice translations. For the explicit calculation of $Z$, we consider the generating function
\begin{aligned} Z[j] &=\left(\frac{m}{2 \pi \varepsilon}\right)^{n / 2} \int \mathrm{d}^{n} q \exp \left{-\frac{1}{2}(\boldsymbol{q}, \mathrm{A} q)+(\boldsymbol{j}, \boldsymbol{q})\right} \ &=\frac{(m / \varepsilon)^{n / 2}}{\sqrt{\operatorname{det} \mathrm{A}}} \exp \left{\frac{1}{2}\left(j, \mathrm{~A}^{-1} \boldsymbol{j}\right)\right} \end{aligned}

## 物理代写|量子场论代写Quantum field theory代考|Problems

2.1 (Gaussian Integral) Show that
$$\int \mathrm{d} z_{1} \mathrm{~d} \bar{z}{1} \ldots \mathrm{d} z{n} \mathrm{~d} \bar{z}{n} \exp \left(-\sum{i j} \bar{z}{i} A{i j} z_{j}\right)=\pi^{n}(\operatorname{det} \mathrm{A})^{-1}$$
with A being a positive Hermitian $n \times n$ matrix and $z_{i}$ complex integration variables.
2.2 (Harmonic Oscillator) In (2.43) we quoted the result for the kernel $K_{\omega}\left(\tau, q^{\prime}, q\right)$ of the $d$-dimensional harmonic oscillator with Hamiltonian
$$\hat{H}=\frac{1}{2 m} \hat{p}^{2}+\frac{m \omega^{2}}{2} \hat{q}^{2}$$
at imaginary time $\tau$. Derive this formula.
Hint: Express the kernel in terms of the eigenfunctions of $\hat{H}$, which for $\hbar=m=$ $\omega=1$ are given by
$$\exp \left(-\xi^{2}-\eta^{2}\right) \sum_{n=0}^{\infty} \frac{\alpha^{n}}{2^{n} n !} H_{n}(\xi) H_{n}(\eta)=\frac{1}{\sqrt{1-\alpha^{2}}} \exp \left(\frac{-\left(\xi^{2}+\eta^{2}-2 \xi \eta \alpha\right)}{1-\alpha^{2}}\right)$$
The functions $H_{n}$ denote the Hermite polynomials.
Comment: This result also follows from the direct evaluation of the path integral.
2.3 (Free Particle on a Circle) A free particle moves on an interval and obeys periodic boundary conditions. Compute the time evolution kernel $K\left(t_{b}-t_{a}, q_{b}, q_{a}\right)=$ $\left\langle q_{b}, t_{b} \mid q_{a}, t_{a}\right\rangle$. Use the familiar formula for the kernel of the free particle (2.26) and enforce the periodic boundary conditions by a suitable sum over the evolution kernel for the particle on $\mathbb{R}$.

## 物理代写|量子场论代写Quantum field theory代考|Thermal Correlation Functions

q^和(τ)=和τH^/ℏq^和−τH^/ℏ,q^和(0)=q^(0),

⟨q^和(τ1)⋯q^和(τn)⟩b≡1从(b)tr⁡(和−bH^q^和(τ1)⋯q^和(τn))

⟨q^和(τ1)q^和(τ2)⟩b=1从(b)tr⁡(和−bH^q^和(τ1)q^和(τ2)) =1从(b)tr⁡(和−(b−τ1)H^q^和−(τ1−τ2)H^q^和−τ2H^)

⟨…⟩b=1从(b)∑n,米和−(b−τ1+τ2)和n和−(τ1−τ2)和米⟨n|q^|米⟩⟨米|q^|n⟩

⟨q^和(τ1)q^和(τ2)⟩b⟶b→∞∑米>0和−(τ1−τ2)(和米−和0)|⟨0|q^|米⟩|2=⟨0|q^和(τ1)q^和(τ2)|0⟩

## 物理代写|量子场论代写Quantum field theory代考|The Harmonic Oscillator

\begin{aligned} Z &=\int \mathrm{d} q_{1} \cdots \mathrm{d} q_{n}\left(\frac{m}{2 \pi \varepsilon}\right)^{ n / 2} \exp \left{-\varepsilon \sum_{j=0}^{n-1}\left(\frac{m}{2}\left(\frac{q_{j+1}-q_ {j}}{\varepsilon}\right)^{2}+\mu q_{j}^{2}\right)\right} \ &=\left(\frac{m}{2 \pi \varepsilon} \right)^{n / 2} \int \mathrm{d} q_{1} \cdots \mathrm{d} q_{n} \exp \left(-\frac{1}{2}(\boldsymbol{q }, \mathrm{A} q)\right) \end{对齐}\begin{aligned} Z &=\int \mathrm{d} q_{1} \cdots \mathrm{d} q_{n}\left(\frac{m}{2 \pi \varepsilon}\right)^{ n / 2} \exp \left{-\varepsilon \sum_{j=0}^{n-1}\left(\frac{m}{2}\left(\frac{q_{j+1}-q_ {j}}{\varepsilon}\right)^{2}+\mu q_{j}^{2}\right)\right} \ &=\left(\frac{m}{2 \pi \varepsilon} \right)^{n / 2} \int \mathrm{d} q_{1} \cdots \mathrm{d} q_{n} \exp \left(-\frac{1}{2}(\boldsymbol{q }, \mathrm{A} q)\right) \end{对齐}

\begin{对齐} Z[j] &=\left(\frac{m}{2 \pi \varepsilon}\right)^{n / 2} \int \mathrm{d}^{n} q \exp \左{-\frac{1}{2}(\boldsymbol{q}, \mathrm{A} q)+(\boldsymbol{j}, \boldsymbol{q})\right} \ &=\frac{(m / \varepsilon)^{n / 2}}{\sqrt{\operatorname{det} \mathrm{A}}} \exp \left{\frac{1}{2}\left(j, \mathrm{~A }^{-1} \boldsymbol{j}\right)\right} \end{aligned}\begin{对齐} Z[j] &=\left(\frac{m}{2 \pi \varepsilon}\right)^{n / 2} \int \mathrm{d}^{n} q \exp \左{-\frac{1}{2}(\boldsymbol{q}, \mathrm{A} q)+(\boldsymbol{j}, \boldsymbol{q})\right} \ &=\frac{(m / \varepsilon)^{n / 2}}{\sqrt{\operatorname{det} \mathrm{A}}} \exp \left{\frac{1}{2}\left(j, \mathrm{~A }^{-1} \boldsymbol{j}\right)\right} \end{aligned}

## 物理代写|量子场论代写Quantum field theory代考|Problems

2.1（高斯积分）证明

∫d和1 d和¯1…d和n d和¯n经验⁡(−∑一世j和¯一世一个一世j和j)=圆周率n(这⁡一个)−1
A 是正厄米特n×n矩阵和和一世复杂的积分变量。
2.2（谐波振荡器）在（2.43）中，我们引用了内核的结果ķω(τ,q′,q)的d具有哈密顿量的维谐振子

H^=12米p^2+米ω22q^2

2.3 （圆周上的自由粒子） 自由粒子在一个区间上移动并服从周期性边界条件。计算时间演化核ķ(吨b−吨一个,qb,q一个)= ⟨qb,吨b∣q一个,吨一个⟩. 对自由粒子的核使用熟悉的公式 (2.26)，并通过粒子在演化核上的适当总和来强制周期性边界条件R.

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

## 物理代写|量子场论代写Quantum field theory代考|PHYS3101

statistics-lab™ 为您的留学生涯保驾护航 在代写量子场论Quantum field theory方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写量子场论Quantum field theory代写方面经验极为丰富，各种代写量子场论Quantum field theory相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 物理代写|量子场论代写Quantum field theory代考|Quantum Mechanics in Imaginary Time

The unitary time evolution operator has the spectral representation
$$\hat{K}(t)=\mathrm{e}^{-\mathrm{i} \hat{H} t}=\int \mathrm{e}^{-\mathrm{i} E t} \mathrm{~d} \hat{P}{\mathrm{E}},$$ where $\hat{P}{\mathrm{E}}$ is the spectral family of the Hamiltonian. If $\hat{H}$ has discrete spectrum, then $\hat{P}{\mathrm{E}}$ is the orthogonal projector onto the subspace of $\mathscr{H}$ spanned by all eigenfunctions with energies less than $E$. In the following we assume that the Hamiltonian operator is bounded from below. Then we can subtract its ground state energy to obtain a non-negative $\hat{H}$ for which the integration limits in (2.35) are 0 and $\infty$. With the substitution $t \rightarrow t-\mathrm{i} \tau$, we obtain $$\mathrm{e}^{-(\tau+\mathrm{i} t) \hat{H}}=\int{0}^{\infty} \mathrm{e}^{-E(\tau+\mathrm{i} t)} \mathrm{d} \hat{P}_{\mathrm{E}}$$
This defines a holomorphic semigroup in the lower complex half-plane
$${z=t-\mathrm{i} \tau \in \mathbb{C}, \tau \geq 0}$$
If the operator $(2.36)$ is known on the negative imaginary axis $(t=0, \tau \geq 0)$, one can perform an analytic continuation to the real axis $(t, \tau=0)$. The analytic continuation to complex time $t \rightarrow-\mathrm{i} \tau$ corresponds to a transition from the Minkowski metric $\mathrm{d} s^{2}=d t^{2}-\mathrm{d} x^{2}-\mathrm{d} y^{2}-\mathrm{d} z^{2}$ to a metric with Euclidean signature. Hence a theory with imaginary time is called Euclidean theory.

The time evolution operator $\hat{K}(t)$ exists for real time and defines a oneparametric unitary group. It fulfills the Schrödinger equation
$$\mathrm{i} \frac{\mathrm{d}}{\mathrm{d} t} \hat{K}(t)=\hat{H} \hat{K}(t)$$
with a complex and oscillating kernel $K\left(t, q^{\prime}, q\right)=\left\langle q^{\prime}|\hat{K}(t)| q\right\rangle$. For imaginary time we have a Hermitian (and not unitary) evolution operator
$$\hat{K}(\tau)=\mathrm{e}^{-\tau \hat{H}}$$
with positive spectrum. The $\hat{K}(\tau)$ exist for positive $\tau$ and form a semi-group only. For almost all initial data, evolution back into the “imaginary past” is impossible.
The evolution operator for imaginary time satisfies the heat equation
$$\frac{\mathrm{d}}{\mathrm{d} \tau} \hat{K}(\tau)=-\hat{H} \hat{K}(\tau)$$
instead of the Schrödinger equation and has kernel
$$K\left(\tau, q^{\prime}, q\right)=\left\langle q^{\prime}\left|\mathrm{e}^{-\tau \hat{H}}\right| q\right\rangle, \quad K\left(0, q^{\prime}, q\right)=\delta\left(q^{\prime}, q\right)$$
This kernel is real ${ }^{1}$ for a real Hamiltonian. Furthermore it is strictly positive:

## 物理代写|量子场论代写Quantum field theory代考|Imaginary Time Path Integral

To formulate the path integral for imaginary time, we employ the product formula $(2.28)$, which follows from the product formula (2.27) through the substitution of it by $\tau$. For such systems the analog of $(2.31)$ for Euclidean time $\tau$ is obtained by the substitution of $i \varepsilon$ by $\varepsilon$. Thus we tind
\begin{aligned} K\left(\tau, q^{\prime}, q\right) &=\left\langle\hat{q}^{\prime}\left|\mathrm{e}^{-\tau \hat{H} / \hbar}\right| \hat{q}\right\rangle \ &=\lim {n \rightarrow \infty} \int \mathrm{d} q{1} \cdots \mathrm{d} q_{n-1}\left(\frac{m}{2 \pi \hbar \varepsilon}\right)^{n / 2} \mathrm{e}^{-S_{\mathrm{E}}\left(q_{0}, q_{1}, \ldots, q_{n}\right) / \hbar} \ S_{\mathrm{E}}(\ldots) &=\varepsilon \sum_{j=0}^{n-1}\left{\frac{m}{2}\left(\frac{q_{j+1}-q_{j}}{\varepsilon}\right)^{2}+V\left(q_{j}\right)\right} \end{aligned}
where $q_{0}=q$ and $q_{n}=q^{\prime}$. The multidimensional integral represents the sum over all broken-line paths from $q$ to $q^{\prime}$. Interpreting $S_{\mathrm{E}}$ as Hamiltonian of a classical lattice model and $\hbar$ as temperature, it is (up to the fixed endpoints) the partition function of a one-dimensional lattice model on a lattice with $n+1$ sites. The realvalued variable $q_{j}$ defined on site $j$ enters the action $S_{\mathrm{E}}$ which contains interactions between the variables $q_{j}$ and $q_{j+1}$ at neighboring sites. The values of the lattice field
$${0,1, \ldots, n-1, n} \rightarrow\left{q_{0}, q_{1}, \ldots, q_{n-1}, q_{n}\right}$$
are prescribed at the endpoints $q_{0}=q$ and $q_{n}=q^{\prime}$. Note that the classical limit $\hbar \rightarrow 0$ corresponds to the low-temperature limit of the lattice system.

The multidimensional integral (2.52) corresponds to the summation over all path on the time lattice. What happens to the finite-dimensional integral when we take the continuum limit $n \rightarrow \infty$ ? Then we obtain the Euclidean path integral representation for the positive kernel
$$K\left(\tau, q^{\prime}, q\right)=\left\langle q^{\prime}\left|\mathrm{e}^{-\tau \hat{H} / h}\right| q\right\rangle=C \int_{q(0)=q}^{q(\tau)=q^{\prime}} \mathscr{D} q \mathrm{e}^{-S_{\mathrm{E}}[q] / h}$$
The integrand contains the Euclidean action
$$S_{\mathrm{E}}[q]=\int_{0}^{\tau} d \sigma\left{\frac{m}{2} \dot{q}^{2}+V(q(\sigma))\right}$$
which for many physical systems is bounded from below.

## 物理代写|量子场论代写Quantum field theory代考|Path Integral in Quantum Statistics

The Euclidean path integral formulation immediately leads to an interesting connection between quantum statistical mechanics and classical statistical physics. Indeed, if we set $\tau / \hbar \equiv \beta$ and integrate over $q=q^{\prime}$ in (2.53), then we end up with the path integral representation for the canonical partition function of a quantum system with Hamiltonian $\hat{H}$ at inverse temperature $\beta=1 / k_{B} T$. More precisely, setting $q=q^{\prime}$ and $\tau=\hbar \beta$ in the left-hand side of this formula, then the integral over $q$ yields the trace of $\exp (-\beta \hat{H})$, which is just the canonical partition function,
$$\int \mathrm{d} q K(\hbar \beta, q, q)=\operatorname{tr} \mathrm{e}^{-\beta \hat{H}}=Z(\beta)=\sum \mathrm{e}^{-\beta E_{n}} \quad \text { with } \quad \beta=\frac{1}{k_{B} T}$$
Setting $q=q^{\prime}$ in the Euclidean path integral in (2.53) means that we integrate over paths beginning and ending at $q$ during the imaginary time interval $[0, \hbar \beta]$. The final integral over $q$ leads to the path integral over all periodic paths with period $\hbar \beta$
$$Z(\beta)=C \oint \mathscr{D} q \mathrm{e}^{-S_{\mathrm{E}}[q] / \hbar}, \quad q(\hbar \beta)=q(0)$$
For example, the kernell of the harmonic oscillator in $(2.43)$ on the diagonal is
$$K_{\omega}(\beta, q, q)=\sqrt{\frac{m \omega}{2 \pi \sinh (\omega \beta)}} \exp \left{-m \omega \tanh (\omega \beta / 2) q^{2}\right}$$
where we used units with $\hbar=1$. The integral over $q$ yields the partition function
\begin{aligned} Z(\beta) &=\sqrt{\frac{m \omega}{2 \pi \sinh (\omega \beta)}} \int \mathrm{d} q \exp \left{-m \omega \tanh (\omega \beta / 2) q^{2}\right} \ &=\frac{1}{2 \sinh (\omega \beta / 2)}=\frac{\mathrm{e}^{-\omega \beta / 2}}{1-\mathrm{e}^{-\omega \beta}}=\mathrm{e}^{-\omega \beta / 2} \sum_{n=0}^{\infty} \mathrm{e}^{-n \omega \beta} \end{aligned}
where we used $\sinh x=2 \sinh x / 2 \cosh x / 2$. A comparison with the spectral sum over all energies in (2.55) yields the energies of the oscillator with (angular) frequency $\omega$,
$$E_{n}=\omega\left(n+\frac{1}{2}\right), \quad n=0,1,2, \ldots$$
For large values of $\omega \beta$, i.e., for very low temperature, the spectral sum is dominated by the contribution of the ground state energy. Thus for cold systems, the free energy converges to the ground state energy
$$F(\beta) \equiv-\frac{1}{\beta} \log Z(\beta) \stackrel{\omega \beta \rightarrow \infty}{\longrightarrow} E_{0}$$
One often is interested in the energies and wave functions of excited states. We now discuss an elegant method to extract this information from the path integral.

## 物理代写|量子场论代写Quantum field theory代考|Quantum Mechanics in Imaginary Time

ķ^(吨)=和−一世H^吨=∫和−一世和吨 d磷^和,在哪里磷^和是哈密顿量的谱族。如果H^有离散谱，则磷^和是在子空间上的正交投影H由能量小于的所有特征函数跨越和. 在下文中，我们假设哈密顿算子是从下面有界的。然后我们可以减去它的基态能量得到一个非负的H^(2.35) 中的积分限制为 0 并且∞. 随着替换吨→吨−一世τ， 我们获得

ķ^(τ)=和−τH^

ddτķ^(τ)=−H^ķ^(τ)

ķ(τ,q′,q)=⟨q′|和−τH^|q⟩,ķ(0,q′,q)=d(q′,q)

## 物理代写|量子场论代写Quantum field theory代考|Imaginary Time Path Integral

\begin{aligned} 左(\tau, q^{\prime}, q\right) &=\left\langle\hat{q}^{\prime}\left|\mathrm{e}^{- \tau \hat{H} / \hbar}\right| \hat{q}\right\rangle\&=\lim{n\rightarrow\infty}\int \mathrm{d}q{1}\cdots\mathrm{d}q_{n-1}\left(\frac {m}{2 \pi \hbar \value psilon}\right)^{n/2}\mathrm{e}^{-S_{\mathrm{E}}\left(q_{0}, q_{1} , \ldots, q_{n}\right) / \hbar}\S_{\mathrm{E}}(\ldots) &=\varepsilon \sum_{j=0}^{n-1}\left{\frac { m}{2}\left(\frac{q_{j+1}-q_{j}}{\valuepsilon}\right)^{2}+V\left(q_{j}\right)\right} \结束{对齐}\begin{aligned} 左(\tau, q^{\prime}, q\right) &=\left\langle\hat{q}^{\prime}\left|\mathrm{e}^{- \tau \hat{H} / \hbar}\right| \hat{q}\right\rangle\&=\lim{n\rightarrow\infty}\int \mathrm{d}q{1}\cdots\mathrm{d}q_{n-1}\left(\frac {m}{2 \pi \hbar \value psilon}\right)^{n/2}\mathrm{e}^{-S_{\mathrm{E}}\left(q_{0}, q_{1} , \ldots, q_{n}\right) / \hbar}\S_{\mathrm{E}}(\ldots) &=\varepsilon \sum_{j=0}^{n-1}\left{\frac { m}{2}\left(\frac{q_{j+1}-q_{j}}{\valuepsilon}\right)^{2}+V\left(q_{j}\right)\right} \结束{对齐}

{0,1, \ldots, n-1, n} \rightarrow\left{q_{0}, q_{1}, \ldots, q_{n-1}, q_{n}\right}{0,1, \ldots, n-1, n} \rightarrow\left{q_{0}, q_{1}, \ldots, q_{n-1}, q_{n}\right}

ķ(τ,q′,q)=⟨q′|和−τH^/H|q⟩=C∫q(0)=qq(τ)=q′Dq和−小号和[q]/H

S_{\mathrm{E}}[q]=\int_{0}^{\tau} d \sigma\left{\frac{m}{2} \dot{q}^{2}+V(q( \sigma))\对}S_{\mathrm{E}}[q]=\int_{0}^{\tau} d \sigma\left{\frac{m}{2} \dot{q}^{2}+V(q( \sigma))\对}

## 物理代写|量子场论代写Quantum field theory代考|Path Integral in Quantum Statistics

∫dqķ(ℏb,q,q)=tr⁡和−bH^=从(b)=∑和−b和n 和 b=1ķ乙吨

K_{\omega}(\beta, q, q)=\sqrt{\frac{m \omega}{2 \pi \sinh (\omega \beta)}} \exp \left{-m \omega \tanh ( \omega \beta / 2) q^{2}\right}K_{\omega}(\beta, q, q)=\sqrt{\frac{m \omega}{2 \pi \sinh (\omega \beta)}} \exp \left{-m \omega \tanh ( \omega \beta / 2) q^{2}\right}

\begin{对齐}Z(\beta)&=\sqrt{\frac{m\omega}{2\pi\sinh(\omega\beta)}}\int\mathrm{d}q\exp\left{- m\omega\tanh(\omega\beta/2)q^{2}\right}\&=\frac{1}2\sinh(\omega\beta/2)}=\mathrm{e}^{- \omega\beta/2}{1-\mathrm{e}^{-\omega\beta}}=\mathrm{e}^{-\omega\beta/2}\sum_{n=0}^{\ infty} \ mathrm {e} ^ {- n \ omega \ beta} \ end {对齐}\begin{对齐}Z(\beta)&=\sqrt{\frac{m\omega}{2\pi\sinh(\omega\beta)}}\int\mathrm{d}q\exp\left{- m\omega\tanh(\omega\beta/2)q^{2}\right}\&=\frac{1}2\sinh(\omega\beta/2)}=\mathrm{e}^{- \omega\beta/2}{1-\mathrm{e}^{-\omega\beta}}=\mathrm{e}^{-\omega\beta/2}\sum_{n=0}^{\ infty} \ mathrm {e} ^ {- n \ omega \ beta} \ end {对齐}

F(b)≡−1b日志⁡从(b)⟶ωb→∞和0

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

## 物理代写|量子场论代写Quantum field theory代考|PHYSICS 3544

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• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 物理代写|量子场论代写Quantum field theory代考|Path Integrals in Quantum and Statistical Mechanics

Already back in 1933 , Dirac asked himself whether the classical Lagrangian and action are as significant in quantum mechanics as they are in classical mechanics $[1,2]$. He observed that for simple systems, the probability amplitude
$$K\left(t, q^{\prime}, q\right)=\left\langle q^{\prime}\left|\mathrm{e}^{-\mathrm{i} \hat{A} t / h}\right| q\right\rangle$$
for the propagation from a point with coordinate $q$ to another point with coordinate $q^{\prime}$ in time $t$ is given by
$$K\left(t, q^{\prime}, q\right) \propto \mathrm{e}^{\mathrm{i} S\left[q_{\mathrm{cl}}\right] / h}$$
where $q_{\mathrm{cl}}$ denotes the classical trajectory from $q$ to $q^{\prime}$. In the exponent the action of this trajectory enters as a multiple of Planck’s reduced constant $h$. For a free particle with Lagrangian
$$L_{0}=\frac{m}{2} \dot{q}^{2}$$ the formula $(2.2)$ is verified easily: A free particle moves with constant velocity $\left(q^{\prime}-q\right) / t$ from $q$ to $q^{\prime}$ and the action of the classical trajectory is
$$S\left[q_{\mathrm{cl}}\right]=\int_{0}^{t} \mathrm{~d} s L_{0}\left[q_{\mathrm{cl}}(s)\right]=\frac{m}{2 t}\left(q^{\prime}-q\right)^{2}$$
The factor of proportionality in $(2.2)$ is then uniquely fixed by the condition $\mathrm{e}^{-\mathrm{i} \hat{H} t / \hbar} \longrightarrow 1$ for $t \rightarrow 0$ which in position space reads
$$\lim {t \rightarrow 0} K\left(t, q^{\prime}, q\right)=\delta\left(q^{\prime}, q\right)$$ Alternatively, it is fixed by the property $\mathrm{e}^{-\mathrm{i} \hat{H} t / h} \mathrm{e}^{-\mathrm{i} \hat{H} s / h}=\mathrm{e}^{-\mathrm{i} \hat{H}(t+s) / h}$ that takes the form $$\int \mathrm{d} u K\left(t, q^{\prime}, u\right) K(s, u, q)=K\left(t+s, q^{\prime}, q\right)$$ in position space. Thus, the correct free particle propagator on a line is given by $$K{0}\left(t, q^{\prime} \cdot q\right)=\left(\frac{m}{2 \pi \mathrm{i} \hbar t}\right)^{1 / 2} \mathrm{c}^{\mathrm{i} m\left(q^{\prime}-q\right)^{2} / 2 h t}$$
Similar results hold for the harmonic oscillator or systems for which $\langle\hat{q}(t)\rangle$ fulfills the classical equation of motion. For such systems $\left\langle V^{\prime}(\hat{q})\right\rangle=V^{\prime}(\langle\hat{q}\rangle)$ holds true. However, for general systems, the simple formula (2.2) must be extended, and it was Feynman who discovered this extension back in 1948. He realized that all paths from $q$ to $q^{\prime}$ (and not only the classical path) contribute to the propagator. This means that in quantum mechanics a particle can potentially move on any path $q(s)$ from the initial to the final destination,
$$q(0)=q \quad \text { and } \quad q(t)=q^{\prime}$$

## 物理代写|量子场论代写Quantum field theory代考|Recalling Quantum Mechanics

There are two well-established ways to quantize a classical system: canonical quantization and path integral quantization. For completeness and later use, we recall the main steps of canonical quantization both in Schrödinger’s wave mechanics and Heisenberg’s matrix mechanics.

A classical system is described by its coordinates $\left{q^{i}\right}$ and momenta $\left{p_{i}\right}$ on phase space $\Gamma$. An observable $O$ is a real-valued function on $\Gamma$. Examples are the coordinates on phase space and the energy $H(q, p)$. We assume that phase space comes along with a symplectic structure and has local coordinates with Poisson brackets
$$\left{q^{i}, p_{j}\right}=\delta_{j}^{i}$$
The brackets are extended to observables on through antisymmetry and the derivation rule ${O P, Q}=O{P, Q}+{O, Q} P$. The evolution in time of an observable is determined by
$$\dot{O}={O, H}, \quad \text { e.g. } \quad \dot{q}^{i}=\left{q^{i}, H\right} \quad \text { and } \quad \dot{p}{i}=\left{p{i}, H\right}$$
In the canonical quantization, functions on phase space are mapped to operators, and the Poisson brackets of two functions become commutators of the associated operators:
$$O(q, p) \rightarrow \hat{O}(\hat{q}, \hat{p}) \quad \text { and } \quad{O, P} \longrightarrow \frac{1}{\mathrm{i} \hbar}[\hat{O}, \hat{P}]$$

The time evolution of an (not explicitly time-dependent) observable is determined by Heisenberg’s equation
$$\frac{\mathrm{d} \hat{O}}{\mathrm{~d} t}=\frac{\mathrm{i}}{\hbar}[\hat{H}, \hat{O}]$$
In particular the phase space coordinates $\left(q^{l}, p_{i}\right)$ become operators with commutation relations $\left[\hat{q}^{i}, \hat{p}{j}\right]=\mathrm{i} \hbar \delta{j}^{i}$, and their time evolution is determined by
$$\frac{\mathrm{d} \hat{q}^{i}}{\mathrm{~d} t}=\frac{\mathrm{i}}{\hbar}\left[\hat{H}, \hat{q}^{i}\right] \quad \text { and } \quad \frac{\mathrm{d} \hat{p}{i}}{\mathrm{~d} t}=\frac{\mathrm{i}}{\hbar}\left[\hat{H}, \hat{p}{i}\right]$$
For a system of non-relativistic and spinless particles, the Hamiltonian reads
$$\hat{H}=\hat{H}{0}+\hat{V} \quad \text { with } \quad \hat{H}{0}=\frac{1}{2 m} \sum \hat{p}{i}^{2}$$ and one arrives at Heisenberg’s equations of motion $$\frac{\mathrm{d} \hat{q}^{i}}{\mathrm{~d} t}=\frac{\hat{p}{i}}{m} \quad \text { and } \quad \frac{\mathrm{d} \hat{p}{i}}{\mathrm{~d} t}=-\hat{V}{, i}$$
Observables are represented by Hermitian operators on a Hilbert space $\mathscr{H}$, whose elements characterize the states of the system:
$$\hat{O}(\hat{q}, \hat{p}): \mathcal{H} \longrightarrow \mathcal{H}$$
Consider a particle confined to an endless wire. Its Hilbert space is $\mathcal{H}=L_{2}(\mathbb{R})$, and its position and momentum operator are represented in position space as
$$(\hat{q} \psi)(q)=q \psi(q) \quad \text { and } \quad(\hat{p} \psi)(q)=\frac{\hbar}{i} \partial_{q} \psi(q)$$

## 物理代写|量子场论代写Quantum field theory代考|Feynman–Kac Formula

We shall derive Feynman’s path integral representation for the unitary time evolution operator $\exp (-\mathrm{i} \hat{H} t)$ as well as Kac’s path integral representation for the positive operator $\exp (-\hat{H} \tau)$. Thereby we shall utilize the product formula of Trotter. In case of matrices, this formula was already verified by Lie and has the form:
Theorem 2.1 (Lie’s Theorem) For two matrices $\mathrm{A}$ and $\mathrm{B}$
$$\mathrm{e}^{\mathrm{A}+\mathrm{B}}=\lim {n \rightarrow \infty}\left(\mathrm{e}^{\mathrm{A} / n} \mathrm{e}^{\mathrm{B} / n}\right)^{n}$$ To prove this theorem, we define for each $n$ the two matrices $\mathrm{S}{n}:=\exp (\mathrm{A} / n+\mathrm{B} / n)$ and $\mathrm{T}{n}:=\exp (\mathrm{A} / n) \exp (\mathrm{B} / n)$ and telescope the difference of their $n$ ‘th powers, $$\mathrm{S}{n}^{n}-\mathrm{T}{n}^{n}=\mathrm{S}{n}^{n-1}\left(\mathrm{~S}{n}-\mathrm{T}{n}\right)+\mathrm{S}{n}^{n-2}\left(\mathrm{~S}{n}-\mathrm{T}{n}\right) \mathrm{T}{n}+\cdots+\left(\mathrm{S}{n}-\mathrm{T}{n}\right) \mathrm{T}{n}^{n-1}$$ Now we choose any (sub-multiplicative) matrix norm, for example, the Frobenius norm. The triangle inequality together with $|X Y| \leq|X \mid| Y |$ imply the inequality $|\exp (X)| \leq \exp (|X|)$ such that $$\left|\mathrm{S}{n}\right|,\left|\mathrm{T}{n}\right| \leq a^{1 / n} \quad \text { with } \quad a=\mathrm{e}^{|\mathrm{A}|+|\mathrm{B}|}$$ Thus we conclude $$\left|\mathrm{S}{n}^{n}-\mathrm{T}{n}^{n}\right| \equiv\left|\mathrm{e}^{\mathrm{A}+B}-\left(\mathrm{e}^{\mathrm{A} / n} \mathrm{e}^{B / n}\right)^{n}\right| \leq n \times a^{(n-1) / n}\left|\mathrm{~S}{n}-\mathrm{T}{n}\right|$$ Finally, using $\mathrm{S}{n}-\mathrm{T}_{n}=-[\mathrm{A}, \mathrm{B}] / 2 n^{2}+O\left(1 / n^{3}\right)$, the product formula is verified for matrices. But the theorem also holds for self-adjoint operators.

Theorem $2.2$ (Trotter’s Theorem) If $\hat{A}$ and $\hat{B}$ are self-adjoint operators and $\hat{A}+$ $\hat{B}$ is essentially self-adjoint on the intersection $\mathscr{D}$ of their domains, then
$$\mathrm{e}^{-\mathrm{i} t(\hat{A}+\hat{B})}=s-\lim {n \rightarrow \infty}\left(\mathrm{e}^{-\mathrm{i} t \hat{A} / n} \mathrm{e}^{-\mathrm{i} t \hat{B} / n}\right)^{n}$$ If in addition $\hat{A}$ and $\hat{B}$ are bounded from below, then $$\mathrm{e}^{-\tau(\hat{A}+\hat{B})}=s-\lim {n \rightarrow \infty}\left(\mathrm{e}^{-\tau \hat{A} / n} \mathrm{e}^{-\tau \hat{B} / n}\right)^{n}$$
The operators need not be bounded and the convergence is with respect to the strong operator topology. For operators $\hat{A}{n}$ and $\hat{A}$ on a common domain $\mathscr{D}$ in the Hilbert space, we have s- $\lim {n \rightarrow \infty} \hat{A}{n}=\hat{A}$ iff $\left|\hat{A}{n} \psi-\hat{A} \psi\right| \rightarrow 0$ for all $\psi \in \mathscr{D}$. Formula (2.27) is used in quantum mechanics, and formula $(2.28)$ finds its application in statistical physics and the Euclidean formulation of quantum mechanics [16].

Let us assume that $\hat{H}$ can be written as $\hat{H}=\hat{H}{0}+\hat{V}$ and apply the product formula to the evolution kernel in (2.22). With $\varepsilon=t / n$ and $\hbar=1$, we obtain \begin{aligned} K\left(t, q^{\prime}, q\right) &=\lim {n \rightarrow \infty}\left\langle q^{\prime}\left|\left(\mathrm{e}^{-\mathrm{i} \varepsilon \hat{H}{0}} \mathrm{e}^{-\mathrm{i} \varepsilon \hat{V}}\right)^{n}\right| q\right\rangle \ &=\lim {n \rightarrow \infty} \int \mathrm{d} q_{1} \cdots \mathrm{d} q_{n-1} \prod_{j=0}^{j=n-1}\left|q_{j+1}\right| \mathrm{e}^{-\mathrm{i} \varepsilon \hat{H}{0}} \mathrm{e}^{-i \varepsilon \hat{V}}\left|q{j}\right\rangle \end{aligned}
where we repeatedly inserted the resolution of the identity $(2.21)$ and denoted the initial and final point by $q_{0}=q$ and $q_{n}=q^{\prime}$, respectively. The potential $\hat{V}$ is diagonal in position space such that
$$\left\langle q_{j+1}\left|\mathrm{e}^{-\mathrm{i} \varepsilon \hat{H}{0}} \mathrm{e}^{-\mathrm{i} \varepsilon \hat{V}}\right| q{j}\right\rangle=\left\langle q_{j+1}\left|\mathrm{e}^{-\mathrm{i} \varepsilon \hat{H}{0}}\right| q{j}\right\rangle \mathrm{e}^{-\mathrm{i} \varepsilon V\left(q_{j}\right)}$$

## 物理代写|量子场论代写Quantum field theory代考|Path Integrals in Quantum and Statistical Mechanics

ķ(吨,q′,q)=⟨q′|和−一世一个^吨/H|q⟩

ķ(吨,q′,q)∝和一世小号[qCl]/H

∫d在ķ(吨,q′,在)ķ(s,在,q)=ķ(吨+s,q′,q)在位置空间。因此，一条线上正确的自由粒子传播子由下式给出

ķ0(吨,q′⋅q)=(米2圆周率一世ℏ吨)1/2C一世米(q′−q)2/2H吨

q(0)=q 和 q(吨)=q′

## 物理代写|量子场论代写Quantum field theory代考|Recalling Quantum Mechanics

\left{q^{i}, p_{j}\right}=\delta_{j}^{i}\left{q^{i}, p_{j}\right}=\delta_{j}^{i}

○(q,p)→○^(q^,p^) 和 ○,磷⟶1一世ℏ[○^,磷^]

（不是明确的时间相关的）可观测的时间演化由海森堡方程确定

d○^ d吨=一世ℏ[H^,○^]

dq^一世 d吨=一世ℏ[H^,q^一世] 和 dp^一世 d吨=一世ℏ[H^,p^一世]

H^=H^0+在^ 和 H^0=12米∑p^一世2一个到达海森堡的运动方程

dq^一世 d吨=p^一世米 和 dp^一世 d吨=−在^,一世
Observables 由希尔伯特空间上的 Hermitian 算子表示H，其元素表征系统的状态：

○^(q^,p^):H⟶H

(q^ψ)(q)=qψ(q) 和 (p^ψ)(q)=ℏ一世∂qψ(q)

## 物理代写|量子场论代写Quantum field theory代考|Feynman–Kac Formula

Theorem 2.1 (Lie’s Theorem) 对于两个矩阵一个和乙

|小号n|,|吨n|≤一个1/n 和 一个=和|一个|+|乙|因此我们得出结论

|小号nn−吨nn|≡|和一个+乙−(和一个/n和乙/n)n|≤n×一个(n−1)/n| 小号n−吨n|最后，使用小号n−吨n=−[一个,乙]/2n2+○(1/n3)，乘积公式针对矩阵进行验证。但该定理也适用于自伴算子。

ķ(吨,q′,q)=林n→∞⟨q′|(和−一世eH^0和−一世e在^)n|q⟩ =林n→∞∫dq1⋯dqn−1∏j=0j=n−1|qj+1|和−一世eH^0和−一世e在^|qj⟩

⟨qj+1|和−一世eH^0和−一世e在^|qj⟩=⟨qj+1|和−一世eH^0|qj⟩和−一世e在(qj)

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

## 物理代写|电磁学代写electromagnetism代考|PHYSICS2534

statistics-lab™ 为您的留学生涯保驾护航 在代写电磁学electromagnetism方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写电磁学electromagnetism代写方面经验极为丰富，各种代写电磁学electromagnetism相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 物理代写|电磁学代写electromagnetism代考|Electrostatics of Conductors

Topics. The electrostatic potential in vacuum. The uniqueness theorem for Poisson’s equation. Laplace’s equation, harmonic functions and their properties. Boundary conditions at the surfaces of conductors: Dirichlet, Neumann and mixed boundary conditions. The capacity of a conductor. Plane, cylindrical and spherical capacitors. Electrostatic field and electrostatic pressure at the surface of a conductor. The method of image charges: point charges in front of plane and spherical conductors.
Basic equations Poisson’s equation is
$$\nabla^{2} \varphi(\mathbf{r})=-4 \pi k_{\mathrm{e}} \varrho(\mathbf{r})$$
where $\varphi(\mathbf{r})$ is the electrostatic potential, and $\varrho(\mathbf{r})$ is the electric charge density, at the point of vector position $\mathbf{r}$. The solution of Poisson’s equation is unique if one of the following boundary conditions is true

1. Dirichlet boundary condition: $\varphi$ is known and well defined on all of the boundary surfaces.
2. Neumann boundary condition: $\mathbf{E}=-\nabla \varphi$ is known and well defined on all of the boundary surfaces.
3. Modified Neumann boundary condition (also called Robin boundary condition): conditions where boundaries are specified as conductors with known charges.
4. Mixed boundary conditions: a combination of Dirichlet, Neumann, and modified Neumann boundary conditions:
Laplace’s equation is the special case of Poisson’s equation
$$\nabla^{2} \varphi(\mathbf{r})=0$$
which is valid in vacuum.

## 物理代写|电磁学代写electromagnetism代考|Metal Sphere in an External Field

A a metal sphere of radius $R$ consists of a “rigid” lattice of ions, each of charge $+Z e$, and valence electrons each of charge $-e$. We denote by $n_{\mathrm{i}}$ the ion density, and by $n_{\mathrm{e}}$ the electron density. The net charge of the sphere is zero, therefore $n_{\mathrm{e}}=Z n_{\mathrm{i}}$. The sphere is located in an external, constant, and uniform electric field $\mathbf{E}{0}$. The field causes a displacement $\delta$ of the “electron sea” with respect to the ion lattice, so that the total field inside the sphere, $\mathbf{E}$, is zero. Using Problem $1.1$ as a model, evaluate a) the displacement $\delta$, giving a numerical estimate for $E{0}=10^{3} \mathrm{~V} / \mathrm{m}$;
b) the field generated by the sphere at its exterior, as a function of $\mathbf{E}_{0}$;
c) the surface charge density on the sphere.

(b) Consider the configurations of
(c)
a) A charge $q$ is located at a distance $a$ from an infinite conducting plane.
b) Two opposite charges $+q$
Fig. $2.1$ and $-q$ are at a distance $d$ from distance $a$ from an infinite conducting plane.
c) A charge $q$ is at distances $a$ and $b$, respectively, from two infinite conducting half planes forming a right dihedral angle.

## 物理代写|电磁学代写electromagnetism代考|Fields Generated by Surface Charge Densities

Consider the case a) of Problem 2.2: we have a point charge $q$ at a distance $a$ from an infinite conducting plane.
a) Evaluate the surface charge density $\sigma$, and the total induced charge $q_{\text {ind }}$, on the plane.

b) Now assume to have a nonconducting plane with the same surface charge distribution as in point a). Find the electric field in the whole space.
c) A non conducting spherical surface of radius $a$ has the same charge distribution as the conducting sphere of Problem 2.4. Evaluate the electric field in the whole space.

A point charge $q$ is located at a distance $d$ from the center of a conducting grounded sphere of radius $a<d$. Evaluate
a) the electric potential $\varphi$ over the whole space;
b) the force on the point charge;
c) the electrostatic energy of the system.
Answer the above questions also in the case of an isolated, uncharged conducting sphere.

An electric dipole $\mathbf{p}$ is located at a distance $d$ from the center of a conducting sphere of radius $a$. Evaluate the electrostatic potential $\varphi$ over the whole space assuming that
a) $\mathbf{p}$ is perpendicular to the direction from $\mathbf{p}$ to the center of the sphere,
b) $\mathbf{p}$ is directed towards the center of the sphere.
c) $\mathbf{p}$ forms an arbitrary angle $\theta$ with respect to the straight line passing through the center of the sphere and the dipole location.

In all three cases consider the two possibilities of i) a grounded sphere, and ii) an electrically uncharged isolated sphere.

## 物理代写|电磁学代写electromagnetism代考|Electrostatics of Conductors

∇2披(r)=−4圆周率ķ和ϱ(r)

1. 狄利克雷边界条件：披是已知的并且在所有的边界表面上定义良好。
2. 纽曼边界条件：和=−∇披是已知的并且在所有的边界表面上定义良好。
3. 修正的 Neumann 边界条件（也称为 Robin 边界条件）：边界被指定为具有已知电荷的导体的条件。
4. 混合边界条件：Dirichlet、Neumann 和修正的 Neumann 边界条件的组合：
拉普拉斯方程是泊松方程的特例
∇2披(r)=0
这在真空中是有效的。

## 物理代写|电磁学代写electromagnetism代考|Metal Sphere in an External Field

A 一个半径为金属的球体R由离子的“刚性”晶格组成，每个电荷+从和, 和价电子，每个电荷−和. 我们表示n一世离子密度，并由n和电子密度。球体的净电荷为零，因此n和=从n一世. 球体位于一个外部的、恒定的、均匀的电场中和0. 该场导致位移d相对于离子晶格的“电子海”，因此球体内的总场，和, 为零。使用问题1.1作为模型，评估 a) 位移d, 给出一个数值估计和0=103 在/米;
b) 球体在其外部产生的场，作为以下函数的函数和0;
c) 球面上的表面电荷密度。

(b) 考虑
(c)
a) 电荷的配置q位于远处一个从一个无限的导电平面。
b) 两个相反的电荷+q

c) 收费q在远处一个和b，分别来自形成直二面角的两个无限导电半平面。

## 物理代写|电磁学代写electromagnetism代考|Fields Generated by Surface Charge Densities

a) 评估表面电荷密度σ, 和总感应电荷q工业 ， 在飞机上。

b) 现在假设有一个非导电平面，其表面电荷分布与 a) 点相同。求整个空间的电场。
c) 半径为非导电球面一个具有与问题 2.4 的导电球相同的电荷分布。评估整个空间中的电场。

a) 电位披覆盖整个空间；
b) 点电荷上的力；
c) 系统的静电能。

a)p垂直于从p到球心，
b)p指向球体的中心。
C）p形成任意角度θ关于通过球心和偶极子位置的直线。

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

## 物理代写|电磁学代写electromagnetism代考|PHYS3040

statistics-lab™ 为您的留学生涯保驾护航 在代写电磁学electromagnetism方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写电磁学electromagnetism代写方面经验极为丰富，各种代写电磁学electromagnetism相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 物理代写|电磁学代写electromagnetism代考|Mie Oscillations

Now, instead of a the metal slab of Problem 1.4, consider a metal sphere of radius $R$. Initially, all the conduction electrons ( $n_{\mathrm{e}}$ per unit volume) are displaced by $-\delta$ (with $\delta \ll R$ ) by an external electric field, analogously to Problem 1.1.
a) At time $t=0$ the external field is suddenly removed. Describe the subsequent motion of the conduction electrons under the action of the self-consistent electrostatic field, neglecting the boundary effects on the electrons close to the surface of the sphere.
b) At the limit $\delta \rightarrow 0$ (but assuming $e n_{\mathrm{e}} \delta=\sigma_{0}$ to remain finite, $\mathrm{i}{2} \mathrm{e}{2}$, the charge distribution is a surface density), find the electrostatic energy of the sphere as a function of $\delta$ and use the result to discuss the electron motion as in point $\mathbf{a})$.

## 物理代写|电磁学代写electromagnetism代考|Coulomb explosions

At $t=0$ we have a spherical cloud of radius $R$ and total charge $Q$, comprising $N$ point-like particles. Each particle has charge $q=Q / N$ and mass $m$. The particle density is uniform, and all particles are at rest.

a) Evaluate the electrostatic potential energy of a charge located at a distance $r0$. Consider the particles located in the infinitesimal spherical shell $r_{0}<r_{\mathrm{s}}<r_{0}+\mathrm{d} r$, with $r_{0}+\mathrm{d} r<R$, at $t=0$. Show that the equation of motion of the layer is
$$m \frac{\mathrm{d}^{2} r_{\mathrm{s}}}{\mathrm{d} t^{2}}=k_{\mathrm{e}} \frac{q Q}{r_{\mathrm{s}}^{2}}\left(\frac{r_{0}}{R}\right)^{3}$$
c) Find the initial position of the particles that acquire the maximum kinetic energy during the cloud expansion, and determinate the value of such maximum energy.
d) Find the energy spectrum, i.e., the distribution of the particles as a function of their final kinetic energy. Compare the total kinetic energy with the potential energy initially stored in the electrostatic field.
e) Show that the particle density remains spatially uniform during the expansion.

## 物理代写|电磁学代写electromagnetism代考|Plane and Cylindrical Coulomb Explosions

Particles of identical mass $m$ and charge $q$ are distributed with zero initial velocity and uniform density $n_{0}$ in the infinite slab $|x|0$ the slab expands because of the electrostatic repulsion between the pairs of particles.
a) Find the equation of motion for the particles, its solution, and the kinetic energy acquired by the particles.
b) Consider the analogous problem of the explosion of a uniform distribution having cylindrical symmetry.

Two rigid spheres have the same radius $R$ and the same mass $M$, and opposite charges $\pm Q$. Both charges are uniformly and rigidly distributed over the volumes of the two spheres. The two spheres are initially at rest, at a distance $x_{0} \gg R$ between their centers, such that their interaction energy is negligible compared to the sum of their “internal” (construction) energies.
a) Evaluate the initial energy of the system.
The two spheres, having opposite charges, attract each other, and start moving at $t=0$.
b) Evaluate the velocity of the spheres when they touch each other (i.e. when the distance between their centers is $x=2 R$ ).
c) Assume that, after touching, the two spheres penetrate each other without friction. Evaluate the velocity of the spheres when the two centers overlap $(x=0)$.

An electrically neutral metal sphere of radius $a$ contains $N$ conduction electrons. A fraction $f$ of the conduction electrons $(0<f<1)$ is removed from the sphere, and the remaining $(1-f) N$ conduction electrons redistribute themselves to an equilibrium configurations, while the $N$ lattice ions remain fixed.
a) Evaluate the conduction-electron density and the radius of their distribution in the sphere.

Now the conduction-electron sphere is rigidly displaced by $\boldsymbol{\delta}$ relatively to the ion lattice, with $|\delta|$ small enough for the conduction-electron sphere to remain inside the ion sphere.
b) Evaluate the electric field inside the conduction-electron sphere.
c) Evaluate the oscillation frequency of the conduction-electron sphere when it is released.

## 物理代写|电磁学代写electromagnetism代考|Mie Oscillations

a) 有时吨=0外场突然被移除。描述导电电子在自洽静电场作用下的后续运动，忽略靠近球体表面的电子的边界效应。
b) 在极限d→0（但假设和n和d=σ0保持有限，一世2和2，电荷分布是表面密度），找到球体的静电能量作为函数d并使用结果来讨论电子运动一个).

## 物理代写|电磁学代写electromagnetism代考|Coulomb explosions

a) 评估位于远处的电荷的静电势能r0. 考虑位于无穷小球壳中的粒子r0<rs<r0+dr， 和r0+dr<R， 在吨=0. 证明层的运动方程为

c) 找出在云膨胀过程中获得最大动能的粒子的初始位置，并确定该最大能量的值。
d) 找出能谱，即粒子的分布作为其最终动能的函数。将总动能与最初存储在静电场中的势能进行比较。
e) 表明粒子密度在膨胀过程中保持空间均匀。

## 物理代写|电磁学代写electromagnetism代考|Plane and Cylindrical Coulomb Explosions

a) 找出粒子的运动方程、它的解以及粒子获得的动能。
b) 考虑具有圆柱对称性的均匀分布爆炸的类似问题。

a) 评估系统的初始能量。

b) 评估球体相互接触时的速度（即，当它们的中心之间的距离为X=2R）。
c) 假设两个球体接触后相互穿透，没有摩擦。计算两个中心重叠时球体的速度(X=0).

a) 评估传导电子密度及其在球体中的分布半径。

b) 评估传导电子球内的电场。
c) 评估传导电子球释放时的振荡频率。

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

## 物理代写|电磁学代写electromagnetism代考|PHYC20014

statistics-lab™ 为您的留学生涯保驾护航 在代写电磁学electromagnetism方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写电磁学electromagnetism代写方面经验极为丰富，各种代写电磁学electromagnetism相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 物理代写|电磁学代写electromagnetism代考|Basics of Electrostatics

Topics. The electric charge. The electric field. The superposition principle. Gauss’s law. Symmetry considerations. The electric field of simple charge distributions (plane layer, straight wire, sphere). Point charges and Coulomb’s law. The equations of electrostatics. Potential energy and electric potential. The equations of Poisson and Laplace. Electrostatic energy. Multipole expansions. The field of an electric dipole.

Units. An aim of this book is to provide formulas compatible with both SI (French: Système International d’Unités) units and Gaussian units in Chapters 1-6, while only Gaussian units will be used in Chapters 7-13. This is achieved by introducing some system-of-units-dependent constants.

The first constant we need is Coulomb’s constant, $k_{\mathrm{e}}$, which for instance appears in the expression for the force between two electric point charges $q_{1}$ and $q_{2}$ in vacuum, with position vectors $\mathbf{r}{1}$ and $\mathbf{r}{2}$, respectively. The Coulomb force acting, for instance, on $q_{1}$ is
$$\mathbf{f}{1}=k{\mathrm{e}} \frac{q_{1} q_{2}}{\left|\mathbf{r}{1}-\mathbf{r}{2}\right|^{2}} \hat{\mathbf{r}}{12},$$ where $k{\mathrm{e}}$ is Coulomb’s constant, dependent on the units used for force, electric charge, and length. The vector $\mathbf{r}{12}=\mathbf{r}{1}-\mathbf{r}{2}$ is the distance from $q{2}$ to $q_{1}$, pointing towards $q_{1}$, and $\hat{r}{12}$ the corresponding unit vector. Coulomb’s constant is $$k{\mathrm{e}}= \begin{cases}\frac{1}{4 \pi \varepsilon_{0}} 8.987 \cdots \times 10^{9} \mathrm{~N} \cdot \mathrm{m}^{2} \cdot \mathrm{C}^{-2} \simeq 9 \times 10^{9} \mathrm{~m} / \mathrm{F} & \text { SI } \ 1 & \text { Gaussian. }\end{cases}$$
Constant $\varepsilon_{0} \simeq 8.854187817620 \cdots \times 10^{-12} \mathrm{~F} / \mathrm{m}$ is the so-called “dielectric permittivity of free space”, and is defined by the formula

$$\varepsilon_{0}=\frac{1}{\mu_{0} c^{2}}$$
where $\mu_{0}=4 \pi \times 10^{-7} \mathrm{H} / \mathrm{m}$ (by definition) is the vacuum magnetic permeability, and $c$ is the speed of light in vacuum, $c=299792458 \mathrm{~m} / \mathrm{s}$ (this is a precise value, since the length of the meter is defined from this constant and the international standard for time).

Basic equations The two basic equations of this Chapter are, in differential and integral form,
$$\begin{array}{ll} \boldsymbol{\nabla} \cdot \mathbf{E}=4 \pi k_{\mathrm{e}} \varrho, & \oint_{S} \mathbf{E} \cdot \mathrm{d} \mathbf{S}=4 \pi k_{\mathrm{e}} \int_{V} \varrho \mathrm{d}^{3} r \ \boldsymbol{\nabla} \times \mathbf{E}=0, & \oint_{C} \mathbf{E} \cdot \mathrm{d} \ell=0 \end{array}$$
where $\mathbf{E}(\mathbf{r}, t)$ is the electric field, and $\varrho(\mathbf{r}, t)$ is the volume charge density, at a point of location vector $\mathbf{r}$ at time $t$. The infinitesimal volume element is $\mathrm{d}^{3} r=\mathrm{d} x \mathrm{~d} y \mathrm{~d} z$. In (1.4) the functions to bẻ iñtēgrâtèd arré evalluateed ovèr añ arbbitrāry volume $V$, or over the surface $S$ enclosing the volume $V$. The function to be integrated in (1.5) is evaluated over an arbitrary closed path $C$. Since $\boldsymbol{\nabla} \times \mathbf{E}=0$, it is possible to define an electric potential $\varphi=\varphi(\mathbf{r})$ such that
$$\mathbf{E}=-\boldsymbol{\nabla} \varphi$$

## 物理代写|电磁学代写electromagnetism代考|Overlapping Charged Spheres

We assume that a neutral sphere of radius $R$ can be regarded as the superposition of two “rigid” spheres: one of uniform positive charge density $+\varrho_{0}$, comprising the nuclei of the atoms, and a second sphere of the same radius, but of negative uniform charge density $-\varrho_{0}$, comprising the electrons. We further assume that its is possible to shift the two spheres relative to each other by a quantity $\delta$, as shown in Fig. 1.1, without perturbing the internal structure of either sphere.

a) in the “inner” region, where the two spheres overlap,
b) in the “outer” region, i.e., outside both spheres, discussing the limit of small displacements $\delta \ll R$.

A sphere of radius $a$ has uniform charge density $\varrho$ over all its volume, excluding a spherical cavity of radius $b<a$, where $\varrho=0$. The center of the cavity, $O_{b}$ is located at a distance d, with $|\mathbf{d}|<(a-b)$, from the center of the sphere, $O_{a}$. The mass distribution of the sphere is proportional to its charge distribution.
a) Find the electric field inside the cavity.
Now we apply an external, uniform electric field $\mathbf{E}_{0}$. Find
b) the force on the sphere,

c) the torque with respect to the center of the sphere, and the torque with respect to the center of mass.

## 物理代写|电磁学代写electromagnetism代考|Energy of a Charged Sphere

A total charge $Q$ is distributed uniformly over the volume of a sphere of radius $R$. Evaluate the electrostatic energy of this charge configuration in the following three alternative ways:
a) Evaluate the work needed to assemble the charged sphere by moving successive infinitesimals shells of charge from infinity to their final location.
b) Evaluate the volume integral of $u_{\mathrm{E}}=|\mathbf{E}|^{2} /\left(8 \pi k_{\mathrm{e}}\right)$ where $\mathbf{E}$ is the electric field [Eq. (1.10)].
c) Evaluate the volume integral of $\rho \phi / 2$ where $\rho$ is the charge density and $\phi$ is the electrostatic potential [Eq. (1.11)]. Discuss the differences with the calculation made in b).

A square metal slab of side $L$ has thickness $h$, with $h \ll L$. The conduction-electron and ion densities in the slab are $n_{\mathrm{e}}$ and $n_{i}=n_{\mathrm{e}} / Z$, respectively, $Z$ being the ion charge.

An external electric field shifts all conduction electrons by the same amount $\delta$, such that $|\delta| \ll h$, perpendicularly to the base of the slab. We assume that both $n_{\mathrm{e}}$ and $n_{i}$ are constant, that the ion lattice is unperturbed by the external field, and that boundary effects are negligible.
a) Evaluate the electrostatic field generated by the displacement of the electrons.
b) Evaluate the electrostatic energy of the system.
Fig. 1.3
Now the external field is removed, and the “electron slab” starts oscillating around its equilibrium position.
c) Find the oscillation frequency, at the small displacement limit $(\delta \ll h)$.

## 物理代写|电磁学代写electromagnetism代考|Basics of Electrostatics

F1=ķ和q1q2|r1−r2|2r^12,在哪里ķ和是库仑常数，取决于力、电荷和长度的单位。向量r12=r1−r2是距离q2至q1, 指向q1， 和r^12对应的单位向量。库仑常数为

ķ和={14圆周率e08.987⋯×109 ñ⋅米2⋅C−2≃9×109 米/F 和  1 高斯。

e0=1μ0C2

∇⋅和=4圆周率ķ和ϱ,∮小号和⋅d小号=4圆周率ķ和∫在ϱd3r ∇×和=0,∮C和⋅dℓ=0

## 物理代写|电磁学代写electromagnetism代考|Overlapping Charged Spheres

a）在“内部”区域，两个球体重叠，
b）在“外部”区域，即在两个球体之外，讨论小位移的限制d≪R.

a) 求空腔内的电场。

b) 球体上的力，

c) 相对于球心的扭矩，以及相对于质心的扭矩。

## 物理代写|电磁学代写electromagnetism代考|Energy of a Charged Sphere

a) 通过将连续的无穷小电荷壳从无穷远移动到它们的最终位置来评估组装带电球体所需的功。
b) 评估体积积分在和=|和|2/(8圆周率ķ和)在哪里和是电场 [Eq. (1.10)]。
c) 评估体积积分ρφ/2在哪里ρ是电荷密度和φ是静电势 [Eq. (1.11)]。讨论与 b) 中计算的差异。

a) 评估由电子位移产生的静电场。
b) 评估系统的静电能量。

c) 找出小位移极限处的振荡频率(d≪H).

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## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

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