Large-scale optimization is ubiquitous in scientific and engineering applications. The end goal in most applications is the solution is a design, control, or inverse problem, constrained by complex high-fidelity models. Achieving this goal is challenging for many reasons, most notably, the computational complexity of the models and their numerous sources of uncertainty. This talk introduces hyper-differential sensitivity analysis (HDSA) as a tool to assess the sensitivity of optimal solutions with respect to uncertainties. Differing from traditional sensitivity analysis, HDSA provides unique insights in the context of the optimization problem rather than simply the model. The mathematical foundations of HDSA are presented, scalable algorithms are developed to manage high dimensional uncertainty, and insightful examples demonstrate the utility of the proposed approach for nonlinear and multi-physics problems.

This abstract describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the abstract do not necessarily represent the views of the U.S. Department of Energy or the United States Government. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia LLC, a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA-0003525. SAND2020-3425 A.

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Meeting ID: 432 122 316