Euler_2D

本程序可用于线性插值,Fortran源码用于插值计算。(Program for linear interpolation.)

改进的burgers模型，非常适合学习UMAT的同志们学习，来自廖公云老师的《ABAQUS有限元软件在道路工程中的应用》(Improved burgers model is ideal for learning UMAT comrades learn from Liao Gongyun teacher "ABAQUS finite element software road works)

用于模拟混凝土单元的弹塑性模型，并有损伤变量，在ABAQUS中umat中输入弹性模量，泊松比与屈服应力即可。(The elastic-plastic model for the simulation of concrete unit, and the damage variable in the ABAQUS umat enter the elastic modulus, Poisson' s ratio and yield stress can be.)

control souce code with LQR

BP神经网络的整个训练过程,matlab编写的元胞自动机，独立成分分析算法降低原始数据噪声。( The entire training process BP neural network, matlab prepared cellular automata, Independent component analysis algorithm reduces the raw data noise.)

静力缩聚法求缩聚后的结构刚度、质量矩阵，以及频率(Static condensation method for structural stiffness after condensation, mass matrix, and the frequency)

带LASSO修正的LARS算法 稀疏表示(LARS with LASSO modification Sparse Representation)

有限元二维静力计算程序，采用四节点线性单元，可以显示变形和应力云图。(Finite element analysis program for 2D problems)

多元数据分析的主分量分析投影，LZ复杂度反映的是一个时间序列中，用MATLAB编写的遗传算法路径规划。( Principal component analysis of multivariate data analysis projection, LZ complexity is reflected in a time sequence, Genetic algorithms using MATLAB path planning.)

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.)