环境模型类好书，个人认为还可以。 Environmental Modeling: Using MATLAB By E. Holzbecher * Publisher: Springer * Number Of Pages: 320 * Publication Date: 2007-10-01 * Sales Rank: 1103822 * ISBN / ASIN: 3540729364 * EAN: 9783540729365 * Binding: Hardcover * Manufacturer: Springer * Studio: Springer * Average Rating: * Total Reviews: Book Description: The book has two aims: to introduce basic concepts of environmental modeling and to facilitate the application of the concepts using modern numerical tools such as MATLAB. It is targeted at all natural scientists dealing with the environment: process and chemical engineers, physicists, chemists, biologists, biochemists, hydrogeologists, geochemists and ecologists. MATLAB was chosen as the major computer tool for modeling, firstly because it is unique in it's capabilities, and secondly because it is available in most academic institutions, in all universities and in the research departments of many companies.

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Modified_IMK_Models 包含DLL可执行文件和修改后的Ibarra-Medina-Krawinkler退化模型（双线性，面向峰，收缩）的相关文档 -IMKBilin：通常用于模拟钢结构部件的行为 -IMKPeakOriented：通常用于模拟钢筋混凝土（RC）组件的行为，这些组件表现出峰值定向的滞后行为 -IMKPinching：通常用于模拟以收缩为特征的任何结构部件的行为 参考： 伊巴拉（Ibarra，LF），麦地那（Medina），RA和克拉维克勒（Krawinkler，H.）（2005年）。 “包含强度和刚度退化的滞后模型。” 地震工程与结构动力学，34（12），1489-1511，Doi：10.1002 / eqe.495。 Lignos，DG和Krawinkler，H。（2011）。 “钢构件的劣化模型可支持地震荷载作用下钢矩框架的倒塌预测。” 结构工程学报

《Modeling Color Difference for Visualization Design》，该论文是2017年的一篇最佳会议论文，对可视化中的色差进行建模，比较有创新性，作为图形学课程报告，由于我专业不是图形方向，对这个不太了解，一开始入手很难理解，花了几天啃出来的重要内容都在报告中说明了~

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计算神经科学：在宾夕法尼亚大学教授的关于计算和理论神经科学的短期本科课程。 介绍MATLAB中的编程，单神经元模型，离子通道模型，基本神经网络和神经解码的编程

This book provides a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis. It also treats exact and approximate inference algorithms at both theoretical and practical levels. The author assumes very little background on the covered subjects, supplying in-depth discussions for theoretically inclined readers and enough practical details to provide an algorithmic cookbook for the system developer.

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The book is designed for a two-course sequence in stochastic models. The first six chapters can form the first course, and the last four chapters, the second course. The book uses a large number of examples to illustrate the concepts as well as computational tools and typical applications. Each chapter also has a large number of exercises collected at the end.