Optimizing High-Dimensional Physics Simulations via Composite Bayesian Optimization

• URL: http://arxiv.org/abs/2111.14911v1
• Date: Mon, 29 Nov 2021 19:29:35 GMT
• Title: Optimizing High-Dimensional Physics Simulations via Composite Bayesian Optimization
• Authors: Wesley Maddox, Qing Feng, Max Balandat
• Abstract summary: Physical simulation-based optimization is a common task in science and engineering. We develop a Bayesian optimization method leveraging tensor-based Gaussian process surrogates and trust region Bayesian optimization to effectively model the image outputs.
• Score: 1.433758865948252
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Physical simulation-based optimization is a common task in science and engineering. Many such simulations produce image- or tensor-based outputs where the desired objective is a function of those outputs, and optimization is performed over a high-dimensional parameter space. We develop a Bayesian optimization method leveraging tensor-based Gaussian process surrogates and trust region Bayesian optimization to effectively model the image outputs and to efficiently optimize these types of simulations, including a radio-frequency tower configuration problem and an optical design problem.

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