vehicle-bridge-coupled

一维的N_S方程的求解程序，是计算流体力学的算例(the N_S equation of one dimension .)

地球物理偏移相关程序包，能实现时间偏移、深度偏移、及逆时偏移等，编写语言：Matlab。(Geophysical migration coding package, time migration, depth migration, reverse time migration, the writing language: Matlab.)

多导体传输线之间的串扰问题，通过BLT方程求解。(Crosstalk between the multi-conductor transmission line problem solving BLT equation.)

分支界限费用矩阵的说明文档，非常实用,建议下载哦(Branch line cost matrix documentation, very practical, it is recommended to download Oh)

LMS算法对线性色散信道的自适应均衡，并且信道的加性噪声为未知(the use of the LMS algorithm for adaptive equalization of a linear dispersive channel that produces (unknown) distortion)

平常练习的一些程序，比较基础，包括求斐波那契数、完数等等(Some of the usual practice procedures, more infrastructure, including seeking Fibonacci numbers, number, etc. End)

21位水仙花数的寻找，里面用到了大数乘法等细节上的东西。(find the 21 digits narcissus.)

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.)

高等边界元法理论里面在求解弹性力学二维三维程序，(Advanced boundary element program)

求解声学问题的边界元源程序，可以为使用边界元法求解位势问题和弹性力学问题提供启发(problem solving acoustic BEM source, for the use of the boundary element method and the potential problems of elasticity to provide inspiration)