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# 物理代写|计算物理代写Computational physics代考|TFY4235 Features

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## 物理代写|计算物理代写Computational physics代考|Features

Piccinini (2015: 11-15) lists six desired features of an account of computing: $o b$ jectivity, explanation, the right things compute, the wrong things don’t compute, miscomputation is explained, and taxonomy. I will discuss these features in a somewhat wider perspective, labeling the desiderata a bit differently. The meaning desideratum, as I will call it, is to explain what it means to say that a physical system computes (Section 1.2.1). The ontological desideratum is to explain the objectivity status of computing systems (Section 1.2.2). The utility desideratum is to elucidate the role (such as an explanatory role) of computational descriptions (Section 1.2.3). While this book is mainly concerned with fulfilling the first desideratum, I will also say something about the others.

## 物理代写|计算物理代写Computational physics代考|Meaning

When we say that certain systems, modules, processes, or mechanisms compute, we mean that they are similar in certain respects to each other. Even more importantly, we want to emphasize that they are different in some respects from other, non-computing systems. Thus, the meaning desideratum boils down to classification conditions that correctly classify cases of computation as well as noncomputation. Piccinini formulates this demand in terms of two criteria:
The right things compute. A good account of computing mechanisms should entail that paradigmatic examples of computing mechanisms, such as digital computers, calculators, both universal and non-universal Turing machines, and finite state automata, compute. (2015: 12)

As Piccinini implies, it is unrealistic to have a precise formulation of necessary and sufficient conditions that will clearly classify every system into one of the two classes. There are disputable and borderline cases, such as lookup tables. We would be extremely pleased if our conditions were to correctly classify “paradigmatic examples” of computing and non-computing cases.

Now, what you include in the class of computing systems-and, even more importantly, in the contrast class of non-computing systems-pretty much determines the account of computing you end up with. Changing the context, that is, the systems included in each class, can lead to very different accounts of computing. To illustrate the point about the relationships between the inclusive (things-that-compute) and contrast (things-that-don’t-compute) classes that you start with, on the one hand, and the account of computation you end up with, on the other, we must digress a little and compare two characterizations of computation.

Gödel characterizes computation procedures as being “mechanical,” which he describes as “purely formal, i.e., refer only to the outward structure of the formulas, not to their meaning, so that they could be applied by someone who knew nothing about mathematics, or by a machine” (1933: 45). Jack Copeland provides a somewhat similar characterization of a mechanical computation procedure, saying that it is one that “demands no insight or ingenuity on the part of the human being carrying it out” (Copeland 2015). ${ }^{9}$ In contrast, Sejnowski, Koch, and Churchland claim that “mechanical and causal explanations of chemical and electrical signals in the brain are different from computational explanations. The chief difference is that a computational explanation refers to the information content of the physical signals” (1988: 1300). These two characterizations are blind to content, while Sejnowski, Koch, and Churchland argue that computational explanations refer to informational content, while mechanical ones do not. Leaving aside the validity of these characterizations, it is worth noting that they arrive at very different, and indeed contrasting, characterizations (assuming, of course, that computational explanations and computational procedures are related). I would like to suggest that the characterizations are different partly because they are made in very different contexts.

## 物理代写|计算物理代写Computational physics代考|Features

Piccinini (2015: 11-15) 列出了计算帐户的六个所需特征：○b客观性，解释，正确的事情计算，错误的事情不计算，错误计算得到解释，分类学。我将从更广泛的角度讨论这些特征，对需求的标签略有不同。我将称它为“desideratum”的意思是解释说物理系统进行计算意味着什么（第 1.2.1 节）。本体论的需要是解释计算系统的客观性状态（第 1.2.2 节）。实用需求是阐明计算描述的作用（例如解释作用）（第 1.2.3 节）。虽然这本书主要关注的是实现第一个愿望，但我也会谈谈其他的。

## 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。