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Optimizing High-Dimensional Physics Simulations via Composite Bayesian Optimization
Wesley Maddox · Qing Feng · Maximilian Balandat

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in Machine Learning and the Physical Sciences »

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 that image with respect to a high-dimensional parameter space. %some parameters. 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 an optical design problem and a radio-frequency tower configuration problem.

Author Information

Wesley Maddox (New York University)
Qing Feng (Facebook)
Maximilian Balandat (Meta)

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