cvf_rter

适用于ABAQUS的自定义材料子程序，适用于显式分析的J2等向强化模型。(ABAQUS apply to the self-defined material subroutine suitable for explicit analysis J2 model to strengthen.)

格子boltzmann方法模拟2D圆柱绕流程序 C++程序d2Q9模型(Lattice boltzmann simulation program 2D flow around a cylinder model of C++ program d2Q9)

fftw 快速傅里叶变换， C#算法 直接调用dll(fftw fuliye bianhuan C# suanfa zhijie dianyong dll wenjian zhijie daoru fangzai bin wenjian jia zhong)

有限元计算控制加载中弧长法（arc-length）的matlab源程序(Loading FEM arc length control method (arc-length) matlab source)

Umat for GTN damage material model in Abaqus by UMAT - STATIC in Levenberg-Marquardt (LM) solution routines from Slatec library

说明：  该文件是用C语言实现快速傅里叶变换的源代码，需要的朋友快下载(The document is used C language to achieve fast Fourier transform of the source code, need friends fast download)

是一种测向交叉定位算法 通过大量阅读论文获得，可以参考。有兴趣，可以一块讨论(Algorithm for DOA Location)

计算离散傅里叶变换的一种快速算法，简称FFT。快速傅里叶变换是1965年由J.W.库利和T.W.图基提出的。采用这种算法能使计算机计算离散傅里叶变换所需要的乘法次数大为减少，特别是被变换的抽样点数N越多，FFT算法计算量的节省就越显著。(A computing discrete Fourier transform fast algorithm, referred to as FFT. Fast Fourier Transform in 1965 by the JW Cooley and TW Tuji Ti out. Using this algorithm enables a computer to calculate the number of multiplications needed for the discrete Fourier transform greatly reduced, especially sampling points N is transformed more and save FFT algorithm to calculate the amount of the more remarkable.)

瑞雷波勘探的书籍，比较清晰的PDF文档，里面含有瑞雷波正演代码，一最小二乘反演代码，对学习瑞雷波非常有帮助。(Rayleigh wave exploration books, clear PDF document, which contains the Rayleigh wave forward code, a least squares inversion code, very helpful for learning Rayleigh wave.)

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(Conjugate gradient method (Conjugate Gradient) is between the steepest descent method and Newton' s method between a method that takes only a first derivative information, but to overcome the slow convergence of the steepest descent method shortcomings, but also avoid the need to store Newton and computing the inverse Hesse matrix and disadvantages, conjugate gradient method is not only to solve large linear equations of the most useful methods, large-scale nonlinear optimization solution is the most efficient algorithms)