第19章 离散相模型 - 副本

安德森的经典计算流体力学理论基础的学习用书。（英文版）(Anderson s classic theory of computational fluid dynamics-based learning book.)

通径分析算法实现，没有gui的小程序，用于数学实验方面的联系，一般计算数学的课程设计(Path analysis algorithm, there is no gui applets, links to mathematical experiments, the general computational mathematics curriculum design)

使用格子boltzmann方法模拟气液两相流运动(Lattice Boltzmann method(LBT) to simulate two phase flow)

高频地波雷达回波信号二维傅里叶变换仿真处理，通过该变换可以得到海上目标的速度和距离信息。(HF ground wave radar echo simulation of two-dimensional Fourier transform processing, the adoption of the sea can transform the speed and distance from goal information.)

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)

频率响应函数计算程序,通过输入结构的响应数据，识别结构的模态参数，matlab(frequency response function，)

计算分维的程序，很好用，可根据需要随意调用(Calculate fractal dimension program, easy to use, can feel free to call)

车间调度问题主程序,需要进行自主参数设计(Shop scheduling problem the main program, the need for self-Parameter Design)

可视化MATLAB的雅可比法矩阵特征值算法(The Jacobian France matrix eigenvalue algorithm)

包括基本的克里金(Kriging)插值法实现代码，仅实现基本方法部分，不包含扩展克里金方法( kriging uses ordinary kriging to interpolate a variable z measured at locations with the coordinates x and y at unsampled locations xi, yi. The function requires the variable vstruct that contains all necessary information on the variogram. vstruct is the forth output argument of the function variogramfit. This is a rudimentary, but easy to use function to perform a simple kriging interpolation. I call it rudimentary since it always includes ALL observations to estimate values at unsampled locations. This may not be necessary when sample locations are not within the autocorrelation range but would require something like a k nearest neighbor search algorithm or something similar. Thus, the algorithms works best for relatively small numbers of observations (100-500). For larger numbers of observations I recommend the use of GSTAT. Note that kriging fails if there are two or more observa)