快速入门CFD

一本讲述人体运动原理，建模与分析的经典英文专著

classical book of Computational Fluid Dynamics

Fundamentals of Computational Intelligence Keller 2016

An easy introduction to Computational Fluid Dynamics. No prior knowledge of CFD is necessary. Covers the basics - grid generation, numerics, incompressible flow and supersonic flow. Motivates the reader with many examples.

计算机接触和冲击力学，Computational-Contact-and-Impact-Mechanics-Fundamentals-of-Modeling-Interfacial-Phenomena-in-Nonlinear-Finite-Element-Analysis，资源来自互联网

这是国外的一本非常优秀的计算塑性（即非弹性）力学书，详细地介绍了一维及三维的基本弹塑性和粘塑性本构模型的计算方法。

有关Grobner基的经典书籍 Grobner基是代数计算中的经典工具。 Grobner Bases A Computational Approach to Commutative Algebra

This whole book aims to bring ideas and algorithms together. I am convinced that they must be taught and learned in the same course. The algorithm clarifies the idea. The old method, separation of responsibilities , no longer works: Not perfect Mathematics courses teach analytical techniques Engineering courses work on real problems Even within computational science there is a separation we don't need: Not efficient Mathematics courses analyze numerical algorithms Engineering and computer science implement the software I believe it is time to teach and learn the reality of computational science and engineering. I hope this book helps to move that beautiful subj ect forward. Thank you for reading it .

Optimal Transport (OT) is a mathematical gem at the interface between probability, analysis and optimization. The goal of that theory is to define geometric tools that are useful to compare probability distributions. Let us briefly sketch some key ideas using a vocabulary that was first introduced by Monge two centuries ago: a probability distribution can be thought of as a pile of sand. Peaks indicate where likely observations are to appear. Given a pair of probability distributions—two different piles of sand— there are, in general, multiple ways to morph, transport or reshape the first pile so that it matches the second. To every such transport we associate an a “global” cost, using the “local” consideration of how much it costs to move a single grain of sand from one location to another. The goal of optimal transport is to find the least costly transport, and use it to derive an entire geometric toolbox for probability distributions.