MeshPy：从Python生成简单网格 MeshPy为Python提供了高质量的三角形和四面体网格生成。 这种类型的网格主要用于有限元仿真代码中，但也有许多其他应用，从计算机图形学到机器人技术。 为了产生二维和三维网格，MeshPy提供的Python接口到三个广泛关注的网格生成器，由J. Shewchuk， 由杭司前二者都包含在包中稍微修改的版本。 包括用于后者的通用网格读取器，以及从Python脚本运行gmsh的简便方法。 有关Christophe 和Jean-Francois Remacle的的界面，请参见gmsh_interop < >。 除了C ++编译器和有效的Python安装，MeshPy没有任何依赖关系。 在安装meshpy之前，您可以使用以下命令安装pybind11： pip install pybind11 从0.91.2版开始，MeshPy还可以与Pyth

DiffGeoOps 该存储库包含本文的Python实现： 该模块为三角2流形实现了三个微分几何算子的离散版本，本文已对此进行了讨论。 他们是： 平均曲率 高斯曲率 主曲率 用法 $ python3 DiffGeoOps.py -h usage: DiffGeoOps.py [-h] --mode MODE [--ops OPS] [--mesh MESH] [--save] [--title TITLE] i [i ...] First, use '--mode 0' to generate files for containing value of the operator and then plot the operatore using '--mode 1' and '--mesh'. For '--ops', the operations are encoded as: - 1: Mean Curvature - 2: Gaussian Curvature - 3:

Numerical Recipes - The Art of Scientific Computing(3rd) 英文无水印pdf 第3版 pdf所有页面使用FoxitReader和PDF-XChangeViewer测试都可以打开 本资源转载自网络，如有侵权，请联系上传者或csdn删除 本资源转载自网络，如有侵权，请联系上传者或csdn删除

Numerical Recipes 3rd Edition: The Art of Scientific Computing 科学计算艺术第二版第三版C++、无水印清晰版。附带相对应的源代码。以下为该书英文简介： Do you want easy access to the latest methods in scientific computing? This greatly expanded third edition of Numerical Recipes has it, with wider coverage than ever before, many new, expanded and updated sections, and two completely new chapters. The executable C++ code, now printed in colour for easy reading, adopts an object-oriented style particularly suited to scientific applications. Co-authored by four leading scientists from academia and industry, Numerical Recipes starts with basic mathematics and computer science and proceeds to complete, working routines. The whole book is presented in the informal, easy-to-read style that made earlier editions so popular.

Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib By 作者: Robert Johansson ISBN-10 书号: 1484242459 ISBN-13 书号: 9781484242452 Edition 版本: 2nd ed. 出版日期: 2018-12-25 pages 页数: (700 ) Work with vectors and matrices using NumPy Plot and visualize data with Matplotlib Perform data analysis tasks with Pandas and SciPy Review statistical modeling and machine learning with statsmodels and scikit-learn Optimize Python code using Numba and Cython Cover Front Matter 1. Introduction to Computing with Python 2. Vectors, Matrices, and Multidimensional Arrays 3. Symbolic Computing 4. Plotting and Visualization 5. Equation Solving 6. Optimization 7. Interpolation 8. Integration 9. Ordinary Differential Equations 10. Sparse Matrices and Graphs 11. Partial Differential Equations 12. Data Processing and Analysis 13. Statistics 14. Statistical Modeling 15. Machine Learning 16. Bayesian Statistics 17. Sinal Processing 18. Data Input and Output 19. Code Optimization

数值分析的经典书籍Numerical Analysis：Mathematics of Scientific Computing -Kincaid_Cheney， 数值分析（美）金凯德等著英文第三版及相应中文译本。及两位作者所著的另一本相关书籍：Kincaid_Cheney_Numerical_Mathematics_and_Computing__Sixth_Edition