Applied Mathematics Colloquium
Dr. Bedrich Sousedik, University of Maryland College Park
Friday, February 7, 2014 · 12 - 1 PM
Title: Towards Domain Decomposition Methods for Stochastic Partial Differential Equations
Abstract: Our goal is to develop fast, parallel, iterative algorithms that would allow efficient solution of systems of linear equations obtained from stochastic finite element discretizations. Our strategy and this talk consist of two parts. In the first part of the talk I will present the Adaptive-Multilevel BDDC method, where BDDC stands for Balancing Domain Decomposition by Constraints. As opposed to the original two-level BDDC, the multilevel approach preserves scalability as the number of subdomains increases, and the adaptivity enables detection of troublesome parts of the problem on each decomposition level. In the second part I will discuss the structure of the matrices obtained from stochastic Galerkin discretizations of stochastic
elliptic boundary value problems, and outline pre conditioners that take advantage of the recursive hierarchy in the matrix structure.
Abstract: Our goal is to develop fast, parallel, iterative algorithms that would allow efficient solution of systems of linear equations obtained from stochastic finite element discretizations. Our strategy and this talk consist of two parts. In the first part of the talk I will present the Adaptive-Multilevel BDDC method, where BDDC stands for Balancing Domain Decomposition by Constraints. As opposed to the original two-level BDDC, the multilevel approach preserves scalability as the number of subdomains increases, and the adaptivity enables detection of troublesome parts of the problem on each decomposition level. In the second part I will discuss the structure of the matrices obtained from stochastic Galerkin discretizations of stochastic
elliptic boundary value problems, and outline pre conditioners that take advantage of the recursive hierarchy in the matrix structure.
Room: Mathematics/Psychology 104
