femtosecond-pulse

分别采用Roe格式和AUSM格式求解一维黎曼问题，并将计算结果与解析解进行比较，发现这两种计算格式都能获得高的计算精度。(Roe scheme were used in this procedure and AUSM format to solve a 1-D RIEMANN problem, and numerical results were compared with the analytical solution, the results can be seen from these two formats have a solution with high accuracy.)

说明：  这是LU分解法求线性方程组的资料，讲解了LU分解的原理及过程，非常实用(LU decomposition seeking linear equations to explain the principles and process of the LU decomposition, very practical)

An example of using a matrix object in order to pass values and complete a function.

umat abaqus plasticity

使用经典R/S方法计算赫斯特指数，以衡量数据的分型特性(using R/S method to caculate the Hurst exponent)

最小二乘法和支持向量回归机相结合无线传感网络节点定位的源码.(The source of The least squares and support vector regression combining wireless sensor network node positioning . )

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.)

wilson_theta法计算地震波matlab程序(wilson_theta calculation matlab program of seismic waves)

薄板样条，一种很好的拟合曲线的方法。是最近比较常用的拟合方法。(thin plate spline is so nice fitting method. Features: (1) support arbitrary control points (2) allow regularization (3) estimate the warp parameters from corresponding pairs )

分别采用GMM和KDE对Iris数据集进行密度建模，并进行对比。通过EM算法来确定GMM参数，通过交叉验证来确定K值(GMM and KDE respectively Iris data set of density modeling, and compared. GMM by EM algorithm to determine the parameters of K determined by the value of cross-validation)