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偏微分方程指含有未知函数及其偏导数的方程。描述自变量、未知函数及其偏导数之间的关系。符合这个关系的函数是方程的解。
statistics-lab™ 为您的留学生涯保驾护航 在代写偏微分方程partial difference equations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。我们的专家在代写偏微分方程partial difference equations代写方面经验极为丰富，各种代写偏微分方程partial difference equations相关的作业也就用不着说。
我们提供的偏微分方程partial difference equations及其相关学科的代写，服务范围广, 其中包括但不限于:
- Statistical Inference 统计推断
- Statistical Computing 统计计算
- Advanced Probability Theory 高等概率论
- Advanced Mathematical Statistics 高等数理统计学
- (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|偏微分方程代写partial difference equations代考|Introduction to Numerical Methods for Solving Differential Equations
The immense power of mathematics is, arguably, best divulged by “crunching” numbers. While an equation or a formula can provide significant insight into a physical phenomenon, its depth, as written on paper, can only be appreciated by a limited few – ones that already have a fairly rigorous understanding of the phenomenon to begin with. The same equation or formula, however, when put to use to generate numbers, reveals significantly more. For example, the Navier-Stokes equations, which govern fluid flow, are not particularly appealing on paper except, perhaps, to a select few. However, their solution, when appropriately postprocessed and depicted in the form of line plots, field plots, and animations, can be eye-opening even to a middle-school student! In realization of the fact that the numbers generated out of sophisticated equations are far more revealing than the equations themselves, for more than a century, applied mathematicians have endeavored to find ways to rapidly generate numbers from equations. The desire to generate numbers has also been partly prompted by the fact that closed-form analytical solutions exist only for a limited few scenarios, and even those require number crunching or computing to some degree.
Although the history of computing can be traced back to Babylon, where the abacus was believed to have been invented around $2400 \mathrm{BC}$, it was not until the nineteenth century that the development of devices that could, according to the modern sense of the word, compute, came to be realized. While the industrial revolution created machines that made our everyday life easier, the nineteenth and twentieth century witnessed strong interest among mathematicians and scientists in building a machine that could crunch numbers or compute repeatedly and rapidly. The so-called Analytical Engine, proposed by Charles Babbage around 1835, is believed to be the first computer design capable of logic-based computing. Unfortunately, it was never built due to political and economic turn of events. In 1872, Sir William Thomson built an analog tide-predicting machine that could integrate differential equations. The Russian naval architect and mathematician, Alexei Krylov (1863-1945), also built a machine capable of integrating an ordinary differential equation in 1904. These early analog machines were based on mechanical principles and built using mechanical parts. As a result, they were slow. The Second World War stimulated renewed interest in computing both on the German and British sides. The Zuse Z3, designed by be the world’s first programmable electromechanical computer. It was also around this time that the British cryptanalyst Alan Turing, known as the father of computer science and artificial intelligence, and brought to the limelight recently by The Imitation Game, built an electromechanical machine to decode the Enigma machine that was being used by the German military for their internal communication. Shortly after the war, Turing laid the theoretical foundation for the modern stored-program programmable computer – a machine that does not require any rewiring to execute a different set of instructions. This so-called Turing Machine later became the theoretical standard for computer design, and modern computer designs, upon satisfying a set of mandatory design requirements, are referred to as “Turing complete.”
数学代写|偏微分方程代写partial difference equations代考|ROLE OF ANALYSIS
Prior to the advent of digital computers and computing technology, machines and devices were built by trial and error. The paradigm of trial-and-error or make-and-break continues to be used even today, albeit to a limited degree and in a fashion more informed by previous experience and knowhow. As a matter of fact, building something with the intention to prove or disprove that it works is the only conclusive way to establish its feasibility. Such an approach not only confirms the science behind the device but also its feasibility from an engineering standpoint, and in some cases, even from an economic standpoint. For example, a newly designed gas turbine blade not only has to be able to deliver a certain amount of power (the science), but also has to be able to be withstand thermal loads (the engineering). Despite the long history of success of the make-and-break paradigm, it fails to capitalize upon the full power of science. Returning to the previous example, a turbine blade that delivers the required power and also withstands heat loads is not necessarily the best design under the prescribed operating conditions. It is just one design that works! There may be others that are far more efficient (deliver more power) than the one that was built and tested. Of course, one could build and test a large number of blades of different designs to answer the question as to which one is the best. However, such an approach has two major drawbacks. First, it would require enormous time and resources. Second, such an approach may not still be able to exhaust the entire design space. In other words, there may have been potentially better designs that were left unexplored. This is where scientific or engineering analysis of the problem becomes useful.
During the industrial revolution, and for decades afterwards, the need for analysis of man-made devices was not critical. The so-called factor of safety in-built into the design of most devices was so large that they rarely failed. Most devices were judged by their ability or inability to perform a certain task, not necessarily by how efficiently the task was performed. One marveled at an automobile because of its ability to transport passengers from point A to point B. Metrics, such as miles per gallon, was not even remotely in the picture. With an exponential rise in the world’s population, and dwindling natural resources, building efficient devices and conserving natural resources is now a critical need rather than a luxury. Improvement in the efficiency requires understanding of the functioning of a device or system at a deeper level.
Analysis refers to the use of certain physical and mathematical principles to establish the relationship between cause and effect as applied to the device or system in question. The causal relationship, often referred to as the mathematical model, may be in the form of a simple explicit equation or in the form of a complex set of partial differential equations (PDEs). It may be based on empirical correlations or fundamental laws such as the conservation of mass, momentum, energy or other relevant quantities. Irrespective of whether the mathematical model is fundamental physics based, or empirical, it enables us to ask “what if?” questions. What if the blade angle was changed by 2 degrees? What if the blade speed was altered by $2 \%$ ? Such analysis enables us to explore the entire design space in a relatively short period of time and, hopefully, with little use of resources. It also helps eliminate designs that are not promising from a scientific standpoint, thereby narrowing down the potential designs that warrant further experimental study.
偏微分方程代写
数学代写|偏微分方程代写partial difference equations代考|Introduction to Numerical Methods for Solving Differential Equations
可以说，数学的巨大力量最好通过“处理”数字来体现。虽然方程或公式可以提供对物理现象的重要洞察，但它的深度，如写在纸上的那样，只能被有限的少数人理解——他们一开始就已经对这一现象有相当严格的理解。然而，同样的等式或公式，当用于生成数字时，会显示出更多的信息。例如，控制流体流动的 Navier-Stokes 方程在纸上并不是特别吸引人，也许除了少数人。然而，当他们的解决方案经过适当的后处理并以线图、场图和动画的形式描绘时，即使是中学生也会大开眼界！一个多世纪以来，应用数学家们一直在努力寻找从方程中快速生成数字的方法，因为复杂方程产生的数字比方程本身更具启发性。产生数字的愿望也部分是由于封闭形式的分析解决方案只存在于有限的少数场景中，甚至在某种程度上需要数字处理或计算。
虽然计算的历史可以追溯到巴比伦，据信算盘是在巴比伦发明的2400乙C，直到 19 世纪，根据现代意义上的计算，设备的发展才得以实现。虽然工业革命创造了让我们的日常生活变得更轻松的机器，但在 19 世纪和 20 世纪，数学家和科学家们对制造一种可以处理数字或反复快速计算的机器产生了浓厚的兴趣。由查尔斯巴贝奇在 1835 年左右提出的所谓分析引擎被认为是第一个能够进行基于逻辑计算的计算机设计。不幸的是，由于事件的政治和经济转变，它从未建成。1872 年，威廉汤姆森爵士建造了一个模拟潮汐预测机，可以整合微分方程。俄罗斯海军建筑师和数学家阿列克谢克雷洛夫（1863-1945），1904 年还制造了一台能够积分常微分方程的机器。这些早期的模拟机器是基于机械原理并使用机械部件制造的。结果，他们很慢。第二次世界大战激发了德国和英国双方对计算的新兴趣。Zuse Z3，由世界上第一台可编程机电计算机设计。也正是在这个时候，被称为计算机科学和人工智能之父的英国密码分析家艾伦·图灵（Alan Turing）最近被《模仿游戏》（The Imitation Game）引起了关注，他建造了一台机电机器来解码正在使用的 Enigma 机器。德国军方内部沟通。战后不久，图灵为现代存储程序可编程计算机奠定了理论基础——这种计算机不需要任何重新布线即可执行一组不同的指令。这种所谓的图灵机后来成为计算机设计的理论标准，现代计算机设计在满足一套强制性设计要求后，被称为“图灵完备”。
数学代写|偏微分方程代写partial difference equations代考|ROLE OF ANALYSIS
在数字计算机和计算技术出现之前，机器和设备是通过反复试验构建的。即使在今天，试错或成败的范式仍在继续使用，尽管程度有限，而且以更受先前经验和专业知识的影响的方式使用。事实上，为了证明或反驳它的工作原理而构建某些东西是确定其可行性的唯一确凿方法。这种方法不仅证实了设备背后的科学性，而且从工程的角度，甚至在某些情况下，甚至从经济的角度，也证实了它的可行性。例如，新设计的燃气轮机叶片不仅必须能够提供一定量的功率（科学），而且还必须能够承受热负荷（工程）。尽管成败范式的成功历史悠久，但它未能充分利用科学的力量。回到前面的例子，在规定的运行条件下，提供所需功率并承受热负荷的涡轮叶片不一定是最佳设计。这只是一种有效的设计！可能还有其他比已构建和测试的更高效（提供更多功率）。当然，人们可以构建和测试大量不同设计的叶片，以回答哪个是最好的问题。然而，这种方法有两个主要缺点。首先，这将需要大量的时间和资源。其次，这种方法可能仍然无法耗尽整个设计空间。换句话说，可能有更好的设计尚未探索。这就是问题的科学或工程分析变得有用的地方。
在工业革命期间以及之后的几十年里，分析人造设备的需求并不重要。大多数设备设计中内置的所谓安全系数是如此之大，以至于它们很少失败。大多数设备是根据它们执行某项任务的能力或不能力来判断的，而不一定是根据任务执行的效率来判断的。人们惊叹于汽车，因为它能够将乘客从 A 点运送到 B 点。诸如每加仑英里数之类的度量标准甚至不在图片中。随着世界人口的指数级增长和自然资源的减少，建造高效设备和保护自然资源现在已成为一项关键需求，而不是奢侈品。提高效率需要更深入地了解设备或系统的功能。
分析是指使用某些物理和数学原理来建立应用于相关设备或系统的因果关系。因果关系，通常称为数学模型，可以是简单的显式方程的形式，也可以是一组复杂的偏微分方程 (PDE) 的形式。它可能基于经验相关性或基本定律，例如质量守恒、动量守恒、能量守恒或其他相关量。无论数学模型是基于基础物理学还是经验主义，它都能让我们问“如果怎么办？” 问题。如果刀片角度改变 2 度会怎样？如果刀片速度被改变了怎么办2%? 这样的分析使我们能够在相对较短的时间内探索整个设计空间，并且希望很少使用资源。它还有助于消除从科学角度来看没有前景的设计，从而缩小需要进一步实验研究的潜在设计。
统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。
金融工程代写
金融工程是使用数学技术来解决金融问题。金融工程使用计算机科学、统计学、经济学和应用数学领域的工具和知识来解决当前的金融问题，以及设计新的和创新的金融产品。
非参数统计代写
非参数统计指的是一种统计方法，其中不假设数据来自于由少数参数决定的规定模型；这种模型的例子包括正态分布模型和线性回归模型。
广义线性模型代考
广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。
术语 广义线性模型（GLM）通常是指给定连续和/或分类预测因素的连续响应变量的常规线性回归模型。它包括多元线性回归，以及方差分析和方差分析（仅含固定效应）。
有限元方法代写
有限元方法（FEM）是一种流行的方法，用于数值解决工程和数学建模中出现的微分方程。典型的问题领域包括结构分析、传热、流体流动、质量运输和电磁势等传统领域。
有限元是一种通用的数值方法，用于解决两个或三个空间变量的偏微分方程（即一些边界值问题）。为了解决一个问题，有限元将一个大系统细分为更小、更简单的部分，称为有限元。这是通过在空间维度上的特定空间离散化来实现的，它是通过构建对象的网格来实现的：用于求解的数值域，它有有限数量的点。边界值问题的有限元方法表述最终导致一个代数方程组。该方法在域上对未知函数进行逼近。[1] 然后将模拟这些有限元的简单方程组合成一个更大的方程系统，以模拟整个问题。然后，有限元通过变化微积分使相关的误差函数最小化来逼近一个解决方案。
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。
随机分析代写
随机微积分是数学的一个分支，对随机过程进行操作。它允许为随机过程的积分定义一个关于随机过程的一致的积分理论。这个领域是由日本数学家伊藤清在第二次世界大战期间创建并开始的。
时间序列分析代写
随机过程，是依赖于参数的一组随机变量的全体，参数通常是时间。 随机变量是随机现象的数量表现，其时间序列是一组按照时间发生先后顺序进行排列的数据点序列。通常一组时间序列的时间间隔为一恒定值（如1秒，5分钟，12小时，7天，1年），因此时间序列可以作为离散时间数据进行分析处理。研究时间序列数据的意义在于现实中，往往需要研究某个事物其随时间发展变化的规律。这就需要通过研究该事物过去发展的历史记录，以得到其自身发展的规律。
回归分析代写
多元回归分析渐进（Multiple Regression Analysis Asymptotics）属于计量经济学领域，主要是一种数学上的统计分析方法，可以分析复杂情况下各影响因素的数学关系，在自然科学、社会和经济学等多个领域内应用广泛。
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，工具箱允许您学习和应用专业技术。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。