SC14 New Orleans, LA

The International Conference for High Performance Computing, Networking, Storage and Analysis

Scalable, Adaptive Methods for Forward and Inverse Modeling of Continental-Scale Ice Sheet Flow.

Student: Tobin Isaac (University of Texas at Austin)
Advisor: Omar Ghattas (University of Texas at Austin)
Abstract: Projecting sea-level rise is made difficult by the complexity of accurately modeling ice sheet dynamics for the polar ice sheets and the uncertainty in key, unobservable parameter fields; my research addresses the inference of the basal friction field beneath the Antarctic ice sheet. I develop scalable algorithms and numerical methods that make tractable the calculation of a friction field with quantified uncertainties. These contributions fall in the categories of adaptive mesh refinement (AMR), efficient solvers for nonlinear PDEs, and Bayesian statistical inversion, all with an emphasis on scalability and high performance computing. I have developed algorithms for octree-based AMR that have scaled well to 458K processes on ~30K BG/Q nodes. I have developed a solver for high-order discretizations of the nonlinear Stokes equations of ice sheet dynamics that scales to ~600M dofs. I am developing Hessian-approximation techniques for Bayesian inference for problems whose parameter-to-observable map requires solving systems of PDEs.

Poster: pdf
Summary: pdf

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