Block-Structured Adaptive Mesh Refinement Methods for Partial Differential Equations
Abstract:
In block-structured adaptive mesh refinement (AMR) methods, one discretizes solutions to time-dependent partial differential equations on a nested hierarchy of locally-rectangular grids. The hierarchy changes as a function of space, time, and the solution, thus providing a general and flexible method for representing solutions with multiple length scales. In this talk, we will give an overview of some of the principal issues in designing AMR methods for applied science and engineering problems, including the computational mathematics of AMR methods, and the challenges of developing high-performance software on current accelerator-based high-performance computers.
Bio:
Dr. Phillip Colella is a Senior Scientist in the Computational Research Division at the Lawrence Berkeley National Laboratory, and a Professor in Residence in the Electrical Engineering and Computer Science Department at UC Berkeley. He has developed high-resolution and adaptive numerical algorithms for partial differential equations and numerical simulation capabilities for a variety of applications in science and engineering. He has also participated in the design of high-performance software infrastructure for scientific computing, including software libraries, frameworks, and programming languages.