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考普森教授在这本书中，使用经典的分析方法，给出了严谨的一阶和二阶线性偏微分方程理论方面的知识。(in this book， professor copson gives a rigorous account of the theory of partial differential equations of the first order and of linear partial differential equations of the second order， using the methods of classical analysis.)

基于模式耦合方法建立三维面波传播模型，该代码对于做波场模拟的同学很有用。(This program calculates the wavefield produced by a monochromatic surface wave mode incoming on a 3D cartesian structure. It is based on mode coupling and multiple scattering.)

求解无约束优化问题 wolfe线性搜索代码(Wolfe line search for unconstrained optimization)

安德森的经典计算流体力学理论基础的学习用书。（英文版）(Anderson s classic theory of computational fluid dynamics-based learning book.)

理想流体势流的有限元求解程序,简单易懂，适合初学者对该方法编程的一个初步认识,程序内有详细的注释(Potential flow of ideal fluid finite element solver, easy to understand, this method is suitable for beginners programming a preliminary understanding of the program with detailed notes)

fsolve函数求非线性方程组数值解，已经验证其正确性。(fsolve function for the numerical solution of nonlinear equations)

确定近似最优正则化参数，很好的解决不适用问题(To determine the approximate optimal regularization parameter, it is very good to solve the problem.)

基于有限体积法求解二维欧拉方程，是流体问题的最基本求解方法(The finite volume method to solve two-dimensional Euler equation)

共轭梯度法（Conjugate Gradient）是介于最速下降法与牛顿法之间的一个方法，它仅需利用一阶导数信息，但克服了最速下降法收敛慢的缺点，又避免了牛顿法需要存储和计算Hesse矩阵并求逆的缺点，共轭梯度法不仅是解决大型线性方程组最有用的方法之一，也是解大型非线性最优化最有效的算法之一。 在各种优化算法中，共轭梯度法是非常重要的一种。其优点是所需存储量小，具有步收敛性，稳定性高，而且不需要任何外来参数。(Conjugate gradient method (Conjugate Gradient) is between the steepest descent method between the method and Newton' s method, it takes only a first derivative information, but to overcome the steepest descent method convergence slow shortcomings, but also to avoid the Newton method needs to be stored Hesse and disadvantages of computing inverse matrix and the conjugate gradient method is not only one of the most useful methods to solve large linear equations, solution of large-scale nonlinear optimization is one of the most effective algorithm. In various optimization algorithm, conjugate gradient method is a very important one. The advantage is that a small amount of memory required, with step convergence, high stability, and does not require any external parameters.)

格子玻尔兹曼模型DnQm系列模型中应用较为广泛的D2Q7模型源代码，用于模拟流体的流动。(The D2Q7 model source code of the the Lattice Boltzmann the model DnQm series model is widely used to simulate fluid flow.)