filon_integration

牛顿迭代法，用于求解非线性方程，具体使用方法见程序内部说明。(Newton iteration for solving nonlinear equations, the specific use of the procedure see the internal note.)

abaqus 施加周期性边界条件脚本，通过General_Mpc_Cube输入正确的模型和部件文件运行。(ABAQUS applies periodic boundary conditions to scripts)

有限元计算软件Abaqus USDFLD子程序在复合材料中强度分析中的应用。(Finite element analysis software Abaqus USDFLD subroutine in the composite material strength analysis applications.)

CFD数值计算中关于求解激波问题的基于非结构网格的自适应网格方法相关代码(CFD numerical calculation of the shock problem based on the unstructured grid method of adaptive grid method)

用分离变量法求得波动方程傅里叶级数近似解，改程序可画出示功图(Wave equation is obtained by separation of variables method Fourier series approximation solution, the app can show the indicator diagrams)

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(Conjugate gradient method (Conjugate Gradient) is between the steepest descent method and Newton' s method between a method that takes only a first derivative information, but to overcome the slow convergence of the steepest descent method shortcomings, but also avoid the need to store Newton and computing the inverse Hesse matrix and disadvantages, conjugate gradient method is not only to solve large linear equations of the most useful methods, large-scale nonlinear optimization solution is the most efficient algorithms)

说明：  贝叶斯网络学习算法——k2算法，包括对贝叶斯网络结构的学习，最后生成网络(Bayesian network learning algorithm- k2 algorithms, including Bayesian network structure learning, the last generation network)

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.)

2D single bin packing 问题的4Block算法(4-Block Algorithm for 2D single bin packing Problem)

介绍箱梁的有限元建模 网格划分 边界条件 加载 求解的命令流(Introduction of finite element modeling of the box girder meshing load boundary conditions for solving the command stream)