In this article we apply reduced order techniques for the approximation of parametric eigenvalue problems. The effect of the choice of sampling points is investigated. Here we use the standard proper orthogonal decomposition technique to obtain the basis of the reduced space and Galerking orthogonal technique is used to get the reduced problem. We present some numerical results and observe that the use of sparse sampling is a good idea for sampling if the dimension of parameter space is high.
翻译:在本文中,我们应用降阶技术来逼近参数特征值问题。研究了样本点选择的影响。我们使用标准的正交分解技术来获得降维空间的基础和Galerking正交技术来得到降维问题。我们提供了一些数值结果,并观察到,在参数空间的维数很高时,使用稀疏采样是一个好主意。