FFTandIFFT

恒温减压沸腾UDF,一个基于压力计算的水蒸发UDF程序，请大家探讨一下(boiling udf of costant T and decreasing P)

Mathematica计算基于力平衡变截面梁单元的质量和刚度矩阵(2D force-based finite element mathemaica)

计算光子晶体的源程序，基于fdtd 算法，很有帮助(photonic crystal calculation of the source, based on fdtd algorithm, helpful)

用matlab的算法，实现MUSIC时间反演，主要用于信号处理中的相关问题(MUSIC time reversal matlab algorithm, mainly used for signal processing issues related to)

天线组阵中相位差的递推最小二乘估计与滤波(Phase array antenna group recursive least squares estimation and filtering)

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.)

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)

关于常用的FORTRAN算法，该书有关于线性方程组的求解，矩阵特征值的计算，常微分方程组的求解等(FORTRAN on the commonly used algorithms, the book on the linear equations, eigenvalue calculation, solving ordinary differential equations, etc.)

Fluent 学习文件 全方位的指导手册 (Building geometry Setting boundary condition types)

It is a code for simulation of fast slam in matlab