通过不同规模和不同优化准则的拉丁超立方体最优实验设计，验证改进算法的应用效果。
The application results of the improved algorithm are verified by searching Latin hypercube optimal design of varying scales under different optimization criteria.
为了减少随机抽样的次数并保证蒙特卡罗法的数值模拟精度，对比引入了重要抽样法和拉丁超立方体抽样方法；
To reduce sampling number and assure simulation precision, Importance Sampling method and Latin Hypercube Sampling method are coupled with Neumann expansion SFEM respectively.
为了减少随机抽样的次数并保证蒙特卡罗法的数值模拟精度，对比引入了重要抽样法和拉丁超立方体抽样方法；
To reduce sampling number and assure simulation precision, Importance Sampling method and Latin Hypercube Sampling method are coupled with Neumann expansion SFEM respectively.
应用推荐