cohesive element模拟复合材料损伤-VUMAT

C#编写的三次样条插值函数，TSS法求解对角矩阵方程函数，插值点函数值计算函数。(prepared by the cubic spline interpolation function, TSS method diagonal matrix equation function, interpolation function point value function.)

QT编写功能及界面，实现功能：加、减、乘、除、连加、连减、连乘、连除、加减乘除混合运算、除零报错等功能，代码简单清晰，适用于刚刚接触QT的人(QT write functions and interface functionality: add, subtract, multiply, divide, even increase, even less, and even multiply, even in addition, subtraction, multiplication and division mixed operations, in addition to the zero error function, the code is simple and clear, suitable for just contact The QT)

利用坎门站的潮汐观测数据计算调和常数，并利用主要分潮和浅水分潮进行潮汐预报(Kanmen station observation data using tidal harmonic constants calculated, using mainly tidal and shallow water tidal surges were forecast...)

处理任意两点间多条公交、地铁线路的到达问题此，改程序将一二问综合在一起，方便精简，运行速度快(Treatment between any two many bus, subway lines to reach this issue, change the program integrated with the twelve asked to facilitate streamlined, fast)

用于矩阵的一种cholesky分解方法，第一次上传请多关照(cholesky method for matrix decomposition)

说明：  PoleyFitqw曲3线3拟w合类q库 PolewyFitqw曲w线拟w合t类库 PoelywFitq曲w线拟t合类t库 PoelyFtitq曲er线rw合类t库 PolywrFitq曲q线拟wt合t类库(Pwoly33FiwtPoleetPwolyFit555PolywFit)

采用动量梯度下降算法训练BP网络，采用两种训练方法，即 L-M 优化算法（trainlm）和贝叶斯正则化算法（trainbr），用以训练 BP 网络(Gradient descent algorithm using momentum BP network training, using two training methods, namely, LM optimization algorithm (trainlm) and Bayesian regularization algorithm (trainbr), to facilitate the training of BP network)

计算材料学中用相场法模拟凝固相变以及晶粒长大的开源性程序(Computational Materials Science solidification using phase-field simulation of grain growth phase transitions and open-source program)

已经调试成功.内含m文件，可直接运行，迭代自组织数据分析，仿真图是速度、距离、幅度三维图像。( Has been successful debugging. M contains files can be directly run, Iterative self-organizing data analysis, FIG simulation speed, distance, amplitude three-dimensional image.)

多目标跟踪的粒子滤波器，各种kalman滤波器的设计，采用加权网络中节点强度和权重都是幂率分布的模型。( Multi-target tracking particle filter, Various kalman filter design, Using weighted model nodes in the network strength and weight are power law distribution.)