An overlapping domain decomposition method for the solution of parametric elliptic problems via proper generalized decomposition

被引:4
作者
Discacciati, Marco [1 ]
Evans, Ben J. [1 ]
Giacomini, Matteo [2 ,3 ]
机构
[1] Loughborough Univ, Dept Math Sci, Epinal Way, Loughborough LE11 3TU, England
[2] Univ Politecn Cataluna, ETS Ingn Caminos Canales & Puertos, Lab Calcul Numer LaCaN, Barcelona, Spain
[3] Ctr Int Metodes Numer Engn CIMNE, Barcelona, Spain
基金
英国工程与自然科学研究理事会;
关键词
Reduced order models; Proper generalized decomposition; Domain decomposition methods; Overlapping Schwarz method; Non-intrusiveness; BASIS ELEMENT METHOD; REDUCED BASIS METHOD; MODEL; APPROXIMATION; REDUCTION; NETWORKS;
D O I
10.1016/j.cma.2023.116484
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
A non-intrusive proper generalized decomposition (PGD) strategy, coupled with an overlapping domain decomposition (DD) method, is proposed to efficiently construct surrogate models of parametric linear elliptic problems. A parametric multi-domain formulation is presented, with local subproblems featuring arbitrary Dirichlet interface conditions represented through the traces of the finite element functions used for spatial discretization at the subdomain level, with no need for additional auxiliary basis functions. The linearity of the operator is exploited to devise low-dimensional problems with only few active boundary parameters. An overlapping Schwarz method is used to glue the local surrogate models, solving a linear system for the nodal values of the parametric solution at the interfaces, without introducing Lagrange multipliers to enforce the continuity in the overlapping region. The proposed DD-PGD methodology relies on a fully algebraic formulation allowing for real-time computation based on the efficient interpolation of the local surrogate models in the parametric space, with no additional problems to be solved during the execution of the Schwarz algorithm. Numerical results for parametric diffusion and convection-diffusion problems are presented to showcase the accuracy of the DDPGD approach, its robustness in different regimes and its superior performance with respect to standard high-fidelity DD methods.
引用
收藏
页数:27
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