Wilson_angle

全面介绍有限元网格生成算法的英文教材，可以作为网格生成算法的编程资料(finite element mesh generation has not been taken as a formal subject of teaching in universities,as it encompasses several disciplines including classical geometry, computational geometry and topology, finite element method, data structures and algorithms, computer programming and, to a certain extent, even computer graphics.)

《高等计算力学》教材的源程序,非线性有限元计算程序(" Advanced Computational Mechanics" of the source material)

最优化课程中的最速下降法matlab程序，利用了wolfe搜索和armijo线搜索(Matlab program of steepest descent method in optimization course, using Wolfe search and Armijo line search)

差分进化算法，有测试函数，是遗传算法的改进算法，收敛快，精度高。(Differential evolution algorithm, a test function, is an improved genetic algorithm algorithm, fast convergence, high precision.)

说明：  这是用matlab实现的关于傅立叶变换及其反变换的实例.(This is achieved using Matlab on the Fourier transform and its inverse transform example.)

计算牛顿和拉格朗日插值方法,用户输入插值结点(calculated Newton and Lagrange interpolation methods, user input interpolation nodes)

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)

DOA估计：最大熵算法、最小模算法、最小方差算法。输入： 快拍数据协方差矩阵R，观察范围内各方向导向矢量，三维矩阵phiyz。输出：最小方差谱估计矩阵P。(DOA Estimation: maximum entropy algorithm, the minimum norm algorithm, the minimum variance algorithm. Input: snapshot of the data covariance matrix R, the steering vector of each direction in the observed range of three-dimensional matrix phiyz. Output: the minimum variance spectral estimation matrix P.)

说明：  分维反映了复杂形体占有空间的有效性，它是复杂形体不规则性的量度。它与动力系统的混沌理论交叉结合，相辅相成。它承认世界的局部可能在一定条件下或过程中，在某一方面（形态，结构，信息，功能，时间，能量等）表现出与整体的相似性，它承认空间维数的变化既可以是离散的也可以是连续的，进而拓展了视野。(Fractal dimension reflects the validity of space occupied by complex bodies, which is a measure of irregularity of complex bodies. It combines with chaos theory of dynamic system and complements each other. It recognizes that the part of the world may be similar to the whole in certain conditions or processes (form, structure, information, function, time, energy, etc.). It recognizes that the change of spatial dimension can be discrete or continuous, thus expanding the horizon.)

说明：  siPLS和biPLS是两种用于近红外光谱特征波段筛选的变量选择方法，可筛选出与兴关注指标密切相关的特征波段，并去除无关和干扰变量，提高模型稳健性(Bipls and sipls are two variable selection methods for screening the characteristic intervals of near-infrared spectra, which are closely related to performance indicators, to improve the robustness of models.)