In this paper,a radial basis function method combined with the stochastic Galerkin method is considered to approximate elliptic optimal control problem with random coefficients.This radial basis function method is a m...In this paper,a radial basis function method combined with the stochastic Galerkin method is considered to approximate elliptic optimal control problem with random coefficients.This radial basis function method is a meshfree approach for solving high dimensional random problem.Firstly,the optimality system of the model problem is derived and represented as a set of deterministic equations in high-dimensional parameter space by finite-dimensional noise assumption.Secondly,the approximation scheme is established by using finite element method for the physical space,and compactly supported radial basis functions for the parameter space.The radial basis functions lead to the sparsity of the stiff matrix with lower condition number.A residual type a posteriori error estimates with lower and upper bounds are derived for the state,co-state and control variables.An adaptive algorithm is developed to deal with the physical and parameter space,respectively.Numerical examples are presented to illustrate the theoretical results.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.11701253,11871312,12171042,11971259)by the Higher Education Key Platform and Research Project of Guangdong Province(The Key Project on the New Generation of Information Technology,No.2020zdzx3067)by the Natural Science Foundation of Shandong Province,China(Grant Nos.ZR2018MA007,ZR2023MA086,ZR2022MA005).
文摘In this paper,a radial basis function method combined with the stochastic Galerkin method is considered to approximate elliptic optimal control problem with random coefficients.This radial basis function method is a meshfree approach for solving high dimensional random problem.Firstly,the optimality system of the model problem is derived and represented as a set of deterministic equations in high-dimensional parameter space by finite-dimensional noise assumption.Secondly,the approximation scheme is established by using finite element method for the physical space,and compactly supported radial basis functions for the parameter space.The radial basis functions lead to the sparsity of the stiff matrix with lower condition number.A residual type a posteriori error estimates with lower and upper bounds are derived for the state,co-state and control variables.An adaptive algorithm is developed to deal with the physical and parameter space,respectively.Numerical examples are presented to illustrate the theoretical results.
