mul

重力频率域位场延拓。将观测面的位场数据（a20_mag.gtd）向上延拓3.3m，之后与已知的理论值（a53_mag.grd）进行比较，计算其均方误差；反之，将观测面的位场数据（a53_mag.grd）向下延拓3.3m，之后与已知的理论值（a20_mag.grd）进行比较，计算其均方误差。(gravity frequency field extend)

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)

最小二乘法数据处理软件，翻遍计算的程序，应用于各种优化算法(math caculateor)

说明：  本程序基于matlab进行了声波在固体中的仿真模拟。(This procedure carried out based on sound waves in matlab in the simulation of solid.)

面元法程序，用于计算飞行器气动力、气动力矩系数。(panel code for aero calculation.)

Cavity Matlab SIMPLE algorithm

对病态系统采用了四阶龙格库塔法和蛙跳法进行了仿真，得到了很好的实验结果(Pathological systems using fourth-order Runge-Kutta method and the leapfrog method for the simulation, the experimental results)

CFD数值模拟，经典PISO算法程序源程序代码。(CFD numerical simulation, classic PISO algorithm source code.)

(一) 模拟产生一个正态随机序列X(n)，要求自相关函数满足 画出产生的随机序列波形。 ((A) simulation to generate a normal random sequence X (n), required to meet the autocorrelation function of random sequences generated by the waveform plot.)

％ 奇异值分解 (sigular value decomposition,SVD) 是另一种正交矩阵分解法；SVD是最可靠的分解法， ％ 但是它比QR 分解法要花上近十倍的计算时间。[U,S,V]=svd(A)，其中U和V代表二个相互正交矩阵， ％ 而S代表一对角矩阵。 和QR分解法相同者， 原矩阵A不必为正方矩阵。 ％ 使用SVD分解法的用途是解最小平方误差法和数据压缩。用svd分解法解线性方程组，在Quke2中就用这个来计算图形信息，性能相当的好。在计算线性方程组时，一些不能分解的矩阵或者严重病态矩阵的线性方程都能很好的得到解( Singular value decomposition (sigular value decomposition, SVD) is another orthogonal matrix decomposition method SVD decomposition is the most reliable method, but it takes more than QR decomposition near ten times the computing time. [U, S, V] = svd (A), in which U and V on behalf of two mutually orthogonal matrix, and the S on behalf of a diagonal matrix. And QR decomposition are the same, the original matrix A is no need for the square matrix. The use of SVD decomposition method are used as a solution of least squares error method and data compression. Using SVD decomposition solution of linear equations, in Quke2 on to use this information to calculate the graphics performance quite good. In the calculation of linear equations, some indecomposable matrix or serious pathological matrix of linear equations can be a very good solution)