Optimal and Unstructured High-Order Non-Intrusive Approximations for Uncertain Parameterized Simulations
Technical Report,30 Sep 2015,29 Mar 2019
UNIVERSITY OF UTAH SALT LAKE CITY Salt Lake City, United States
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The approximation and prediction of output quantities of interest in large-scale simulation software is an ongoing challenge in scientific computing. This difficulty is compounded when the simulation software contains numerous tunable input parameters that specify modeling scenarios, geometry, and uncertainty. The main goal of this project is robust and efficient prediction of the variability of quantities of interest with respect to these input parameters. This is primarily accomplished via non-intrusive sampling of models. Straightforward and naive sampling methods often usually yield suboptimal performance and convergence guarantees. This project aims to develop novel, modern sampling strategies that perform well and are provably convergent, ideally without dependence on dimension. Nearing the end of this project, the efficacy of the developed procedures will be tested on realistic parameterized scientific problems.
- Numerical Mathematics
- Computer Programming and Software