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计算正交各向异性材料（如木材，复合材料等）的弹塑性本构(The elastoplastic constitutive relation of orthotropic materials is calculated)

Lasso算法源代码,stanford开发的,绝对可靠(Glmnet for Matlab- Lasso (L1) and elastic-net regularized generalized linear models)

极小 算法（Min-Min）、极大极小算法(Max-Min)、最大时间跨度算法（Max-Int）三种典型的映射算法的算法原理以及计算程序(Minimal algorithm (Min-Min), max-min algorithm (Max-Min), maximum time-span algorithm (Max-Int) three typical mapping algorithm and the algorithm of the calculation program)

ADINA 系统是一个单机系统的程序，用于进行固体、结构、流体以及结构相互作用的流体流动的复杂有限元分析。借助 ADINA 系统，用户无需使用一套有限元程序进行线性动态与静态的结构分析，而用另外的程序进行非线性结构分析，再用其他基于流量的有限元程序进行流体流动分析。此外， ADINA 系统还是最主要的、用于结构相互作用的流体流动的完全耦合分析程序（多物理场）。(ADINA System is a stand-alone system program for performing complex fluid flow finite element solid structure, and fluid structure interaction analysis. With ADINA system, users do not need to use a finite element program linear static and dynamic structural analysis, but with additional nonlinear structural analysis program, and then the other program flow based on finite element analysis of fluid flow. In addition, ADINA system or main, fully coupled analysis program for fluid flow structure interaction (multi-physics).)

时间积分算法在有限元动力学中的应用，可以计算结构的瞬态响应(This class implements a Newmark beta-gamma predictor-corrector time inte- gration scheme, with accelerations as primary unknowns. .A Newmark scheme assigns to the dofs the following 5 unknowns : - the displacement d and its predictor D - the velocity v and its predictor V - the acceletion a . Their expressions are calculated by the dofs in class Dof.)

数值计算各算法的程序,包括:牛顿迭代法,超松驰迭代法,微分法等.对于初学数值分析这门课程的人有很大的帮助.(Numerical calculation procedure of the algorithm, including: Newton iteration, ultra-relaxation iteration method, differential method and so on. Beginner numerical analysis for this course were very helpful.)

Umat Abaqus for plasticity

说明：  fluent中udf的实例 计算曳力的例程 可以作为参考以及udf帮助手册(The example of UDF in fluent can be used as a reference to calculate the traction)

用C++写的矩阵鞍点求法(C wrote saddle point Matrix Method)

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.)