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arXiv · 2111.14911

Optimizing High-Dimensional Physics Simulations via Composite Bayesian Optimization

Wesley Maddox·Qing Feng·Max Balandat
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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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Wesley Maddox, Qing Feng, Max Balandat. 2021-11-29. Optimizing High-Dimensional Physics Simulations via Composite Bayesian Optimization. https://arxiv.org/abs/2111.14911

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