chaos333

说明：  利用python进行因子分析,因子分析可以进行降维!(Using Python factor analysis, factor analysis can reduce dimensionality!)

一个非线性方程的matlap程序，杜芬方程的变异(A nonlinear equations matlap program, variation of Duffing equation)

该程序用于瞬变电磁勘探近区场计算，只要输入不同参数便可以得到近区场的视电阻率(The procedure transient electromagnetic exploration for near field calculations, as long as the input parameters can be different near field apparent resistivity)

国外最近的一篇变步长nlms仿真，该文件有文中的几种仿真，包括基本的仿真，信道突变下的追踪性能和语音回声消除的性能分析(Recent foreign nlms a variable step simulation, the file has the text of several simulations, including basic simulation, channel tracking performance under mutation and voice echo cancellation performance analysis)

说明：  对常微分方程进行数值解的matlab函数(Matlab function for numerical solution of ordinary differential equation)

多相流cfd程序，用fortran实现，有多流体和dem计算功能，当然还有其它更多功能(MFiX is a general-purpose computer code developed at the National Energy Technology Laboratory (NETL) for describing the hydrodynamics, heat transfer and chemical reactions in fluid-solids systems. It has been used for describing bubbling and circulating fluidized beds and spouted beds. MFiX calculations give transient data on the three-dimensional distribution of pressure, velocity, temperature, and species mass fractions. MFiX code is based on a generally accepted set of multiphase flow equations. The code is used as a "test-stand" for testing and developing multiphase flow constitutive equations.)

平面单元四边形八结点单元的应力应变分析,考虑了应力应变梯度(the analysis of 2D plane including stress and strain)

fluent udf 壁面热流通量随时间的变化。 调用fluent udf 里面的宏运算。(fluent udf wall heat flux changes with time. Call fluent udf inside the macro operator.)

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.)

说明：  berkeley加州大学SAP系列软件之SolidSAP，由Edward L.Wilson教授开发的三维结构源程序，功能强大，代码清晰，含学习手册。(SolidSAP, a three-dimensional structure source program developed by Professor Edward L. Wilson, is a software of the SAP series at the University of California, Berkeley. It has powerful functions, clear code and learning manual.)