MFIE

计算二维非问题油内热源问题导热，可以参考学习(Calculation of two-dimensional non thermal problems in oil heat conduction, you can refer to the study)

Umat Abaqus for plasticity

关于超声波倒车雷达测距的，最大信噪比的独立分量分析算法，小波包分析提取振动信号中的特征频率。( About ultrasonic parking radar ranging, SNR largest independent component analysis algorithm, Wavelet packet analysis to extract vibration signal characteristic frequency.)

利用雷诺方程和有限元差分法，计算阶梯滑块的压力分布(Reynolds equation and the use of finite element difference method to calculate the pressure distribution of the slider ladder)

无网格伽辽金法中用到的数值积分，此程序，提供多点高斯积分，可任意选择！(element-free Galerkin method used in the numerical integration of this process, provide more Gaussian integral, can be arbitrary choice.)

用三元组表为结构，实现稀疏矩阵的加法并输出。(Achieve sparse matrix addition)

求解二阶变系数常微分方程,主要用于涡激振动中频率响应的计算(Solving second-order ordinary differential equations with variable coefficients)

fft变换后求出主频，FFT transform to calculate frequency

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.)

对现在非常流行的非平行超平面进行编程，可用于twin svm等非平行问题。(Use for "Nonparallel Support Vector Machines")