Y2M.m

用matlab所编的关于用传输矩阵法计算光子晶体的反射率,文件中将程序写在word文档中,并附带有在matlab下画的图,感觉很有用(using Matlab as in the use of the transfer matrix method photonic crystal reflectivity, Lieutenant General procedures documents written in word document, together with the painting under Matlab plans, feeling useful)

使用龙格-库塔间断Galerkin方法（RKDG）求解无粘流动Euler方程的程序，适用于计算流体力学RKDG方法的学习和研究。(Using the discontinuous Galerkin method (DG) for solving inviscid flow Euler equations procedures applicable to DG methods of study and research.)

说明：  关闭OFDM信号子载波，画出OFDM信号频谱图，查看旁瓣泄露情况(Close OFDM signal sub-carrier OFDM signal spectrum plot, view the situation sidelobe leakage)

可以实现十几位整数的四则运算，效率也较好(Four operations can be more than a dozen integer,and the efficiency is better)

一个采用FLUENT计算气体-液体两相流的算例，附件文件可以直接用FLUENT打开，并附带了计算结果截图。(FLUENT is calculated using a gas- liquid two-phase study, attachment files can be opened directly by FLUENT, and the calculation results with the screenshot.)

说明：  关联维数求解，大组件构建，可以快速得到关联维数的值。(The value of correlation dimension can be obtained quickly by solving the correlation dimension and constructing large components.)

偏微分方程数值解上机实习 运用古典显式差分格式求解二维扩散方程的初边值问题 运用P-R差分格式求解二维扩散方程的初边值问题

主要是关于一些数值计算中所要用到的头文件,希望对大家有所帮助(Mainly on the number of numerical calculation by the use of header files, I hope all of you to help)

四面体网格划分的有限元计算源代码，在将变形体划分为四面体后，输入节点坐标，程序利用弹塑性本构，将载荷和位移关系转变为线性方程组，是一个很好的求解简单三维变形体的程序，还可输出单元变形量，应力等。(Tetrahedral mesh of the finite element method source code, in the deformed tetrahedron is divided into, the input node coordinates, the program using elastic-plastic constitutive, will be the relationship between load and displacement into linear equations, is a very good Solving a simple three-dimensional deformation body procedures, but also the output unit deformation, stress and so on.)

ADAM （Adaptive Moment Estimation）是另外一种自适应学习率算法，它结合动量梯度 下降法，在不同参数方向上采用不同学习率，保留前几次迭代的梯度，能够很好 的适应于稀疏数据。(ADAM (Adaptive Moment Estimation) is another adaptive learning rate algorithm, which combines momentum gradient. The descent method, which uses different learning rates in different parameter directions and retains the gradients of previous iterations, is very good. It is suitable for sparse data.)