【新书推荐】【2020】基于MATLAB应用的固体物理学入门

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固体物理学是研究和预测材料基本物理性质的学科,是现代材料科学的支柱,有着广泛的技术应用。

Solid state physics, the study and prediction of the fundamental physical properties of materials, forms the backbone of modern materials science and has many technological applications.

本文的特点是基于MATLAB(R)的计算方法,包括多种数值技术和仿真方法。

The unique feature of this text is the MATLAB®-based computational approach with several numerical techniques and simulation methods included.

这是非常有效的解决需要可视化和直接动手学习固体物理理论概念的方法。

This is highly effective in addressing the need for visualization and a direct hands-on approach in learning the theoretical concepts of solid state physics.

所有教科书用户都可以免费使用该代码。

The code is freely available to all textbook users.

其它功能:使用计算物理的教学工具,在加强固体物理等高级学科的物理教学中起着重要作用

Additional Features:Uses the pedagogical tools of computational physics that have become important in enhancing physics teaching of advanced subjects such as solid state physics

将可视化和模拟仿真添加到主题中,使学生能够积极参与动手教学

Adds visualization and simulation to the subject in a way that enables students to participate actively in a hand-on approach

涵盖固态物理的基本概念,让学生对主题有更深的理解

Covers the basic concepts of solid state physics and provides students with a deeper understanding of the subject matter

提供贯穿全文的独特示例练习

Provides unique example exercises throughout the text

获得数学解析解

Obtains mathematical analytical solutions

使用编程脚本对重要公式结果进行说明,学生可以自己运行并再现图形和/或模拟仿真

Carries out illustrations of important formulae results using programming scripts that students can run on their own and reproduce graphs and/or simulations

利用MATLAB(R)帮助学生形象化固态过程,应用一定的数值技术使固态物理的学习过程更加有效

Helps students visualize solid state processes and apply certain numerical techniques using MATLAB®, making the process of learning solid state physics much more effective

通过使用章末练习加强章节内讨论的示例

Reinforces the examples discussed within the chapters through the use of end-of-chapter exercises

包括了简单的分析和数值例子、更具挑战性的问题,通过计算问题运行代码,创建新的或修改现有的解决问题或再现某些结果

Includes simple analytical and numerical examples to more challenging ones, as well as computational problems with the opportunity to run codes, create new ones, or modify existing ones to solve problems or reproduce certain results.

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