Název:
Reduced basis solver for stochastic Galerkin formulation of Darcy flow with uncertain material parameters
Autoři:
Béreš, Michal Typ dokumentu: Příspěvky z konference Konference/Akce: Programs and Algorithms of Numerical Mathematics /21./, Jablonec nad Nisou (CZ), 20220619
Rok:
2023
Jazyk:
eng
Abstrakt: In this contribution, we present a solution to the stochastic Galerkin (SG) matrix equations coming from the Darcy flow problem with uncertain material coefficients in the separable form. The SG system of equations is kept in the compressed tensor form and its solution is a very challenging task. Here, we present the reduced basis (RB) method as a solver which looks for a low-rank representation of the solution. The construction of the RB consists of iterative expanding of the basis using Monte Carlo sampling. We discuss the setting of the sampling procedure and an efficient solution of multiple similar systems emerging during the sampling procedure using deflation. We conclude with a demonstration of the use of SG solution for forward uncertainty quantification.
Klíčová slova:
deflated conjugate gradient method; Monte Carlo method; reduced basis method; stochastic Galerkin method Číslo projektu: TK02010118 Poskytovatel projektu: GA TA ČR Zdrojový dokument: Programs and Algorithms of Numerical Mathematics 21 : Proceedings of Seminar, ISBN 978-80-85823-73-8