学术报告

题      目：Robust Preconditioners for Mixed-dimemsional Problems

报  告  人：Xiaozhe Hu  副教授  (邀请人：钟柳强 )

                                  Tufts University

时      间：2022-06-07  09：00-10：00

腾讯会议：727-510-781  密码：220607

摘      要：

       Mixed-dimensional partial differential equations arise in many physical applications, such as flow in fractured porous media and flow in vascularized brain tissue. An essential component, and usually the most time-consuming part of simulating PDEs, is solving the large-scale and ill-conditioned linear systems of equations arising from discretizations. In this work, we generalize the traditional framework of designing preconditioners for the saddle point systems and develop effective preconditioners that are robust with respect to the physical and discretization parameters for mixed-dimensional problems. Numerical experiments are presented to support the theory and demonstrate the robustness of our preconditioners on realistic fracture networks and blood flow in the brain.