BESOpython

用fortran语言编写的传统矩量法求解立方体的RCS的程序。(The fortran language prepared by the traditional method of moments for solving the cube RCS program.)

交错网格PML弹性波模拟地震波形的c语言程序(Staggered grid PML elastic wave seismic waveform simulation)

cressman格点插到格点程序，从一个场插值出另一个分辨率不同的场(interpolates fields to another grid as specified )

编程序，按如下要求来求解n元一次线性方程组（假设方程组具有唯一解）。 （1）方程个数n之值由用户通过键盘输入； （2）方程组存放在“增广矩阵”A之中，而n行n+1列的A存储空间通过new来动态分配，且A的各元素值也由用户通过键盘输入 (Allocation procedures, the following requirements to solve the n-a system of linear equations (assuming a unique solution of equations). (1) the number n of the value equation by the user through the keyboard input (2) equations stored in the augmented matrix A, while n line n+ 1 column A storage space through the new dynamically allocated, and A The value of each element by the user through the keyboard input)

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.)

说明：  使用牛顿迭代法求解非线性方程组，程序简单易懂，初学者都能看明白。(Newton iterative method is used to solve the nonlinear equations. The program is simple and easy to understand for beginners.)

可以用最小二乘法来提取趋势项。在MATLAB中可利用它的函数来计算(Least squares method can be used to extract the trend items. It can be used in MATLAB to calculate the function)

说明：  采用有限体积算法一阶迎风型离散格式对二维 Couette流动进行数值求解.(The first-order upwind discrete scheme of the finite volume algorithm is used to solve the two-dimensional gouette flow)

采用最小二乘方法的高斯曲线拟合算法， 用C 语言编写. 包含子程序.(Gaussian Curving Fitting with C language)

一般线性规划问题具有线性方程组的变量数大于方程个数，这时会有不定的解。当决策变量个数n和约束条件个数m较大时，单纯形法是求解线性规划问题的通用方法。 从线性方程组找出一个个的单纯形，每一个单纯形可以求得一组解，然后再判断该解使目标函数值是增大还是变小了，决定下一步选择的单纯形。通过优化迭代，直到目标函数实现最大或最小值(In general linear programming problems, the number of variables with linear equations is greater than the number of equations. When the number of decision variables n and constraint number m are large, simplex method is the general method for solving linear programming problems. Find out one by one from the linear equations of the simplex, each a simplex can get a set of solution, and then concludes that the solution is the objective function value increases or smaller and decided to choose the next simplex. By optimizing iteration, until the target function achieves the maximum or minimum value.)