Robust Discretization and Fast Solvers for Computable Multi-Physics Models (May 12-16, 2014)
- Franco Brezzi
(University of Pavia)
- Jan Hesthaven
(Ecole Polytechnique Fédérale de Lausanne)
- Michael Holst
(University of California, San Diego)
- Jinchao Xu
(Pennsylvania State University)
Most systems targeted by mathematical modeling in modern science and engineering are fundamentally multi-physical and multi-scale in nature. As such, they involve solving complex coupled, generally nonlinear, systems of partial differential equations (PDEs) built from subsystems of PDEs that mathematically model very different physical processes, often at very different scales.
Recent advances in high-performance computer hardware and advanced numerical algorithms have made it feasible to construct realistic mathematical models and to build corresponding numerical simulation software for these types of complex multi-physics/multi-scale problems. However, developing robust, efficient, and practical numerical algorithms for such simulation software that are capable of tackling these complex mathematical models is still extremely challenging in a number of fundamental ways. For example, we do not yet have robust methods that can handle strong coupling between different physics and/or scales, and we still do not have optimal linear solvers that can reliably and efficiently treat the discretized linearized systems.
This workshop will gather together experts in the core related fields in applied and computational mathematics to exchange ideas regarding the development of robust and efficient numerical schemes that preserve the key physics of these models, and to study the development of fast and efficient linear and nonlinear solvers that are scalable and optimal. This workshop will also target young researchers and members of under-represented groups to help launch their research in this area.