Kalman

迎风格式有限元求解二维不可压缩流，值得学习和借鉴其中的许多内容(upwind finite element method solve incompressible fluent flow)

自适应网格生成的文章，广泛的应用到有限元和无网格方法中(a nice paper of adaptive mesh generation)

大量的有限元法求解偏微分方程的实例程序，程序里有详细的解释语句(lot of finite element method PDE examples of procedures, procedures are detailed statement of explanation)

基于abaqus umat的修正的M-C子程序，《ABAQUS在隧道与地下工程中的应用》-陈卫忠，版权归作者所有(Based on the modified MC abaqus umat subroutine, " ABAQUS in the tunnel and underground engineering" - Chen Weizhong, copyright of all)

说明：  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 Gradient) is a method between the steepest descent method and Newton's method. It only uses the first derivative information, but overcomes the disadvantage of slow convergence of steepest descent method, and avoids the disadvantage of storing and calculating Hesse matrix and solving inverse of Newton's method. Conjugate gradient method is not only one of the most useful methods to solve large-scale linear equations, but also the most effective method to solve large-scale nonlinear optimization One of the algorithms of. Among all kinds of optimization algorithms, conjugate gradient method is very important. It has the advantages of small storage, step convergence, high stability and no external parameters.)

ABAQUS扩展子程序开发，用于计算模拟(ABAQUS extended subroutine application)

这个是计算声传播的各种模型 包括简正波kraken波数积分scooter等模型 有很好的学习价值 (a system program for acoustic propagation calculation)

该程序为fluent软件进行速度入口造波数值模拟，波浪为二阶stokes波(The program for the FLUENT software simulation of wave speed entrance numerical, two order Stokes wave)

基于自适应在线遗传算法整定的PID控制,每个采样时间分别对PD参数进行整定(based on adaptive online genetic algorithm tuning PID control, sampling each time the PD parameters tuning)

2维非结构欧拉求解 fortran代码，用于2维流场求解(2D Unstructured Euler code)