1. Optimizing High-Dimensional Physics Simulations via Composite Bayesian Optimization
- Author
-
Maddox, Wesley, Feng, Qing, and Balandat, Max
- Subjects
FOS: Computer and information sciences ,Computer Science - Machine Learning ,Artificial Intelligence (cs.AI) ,Computer Science - Artificial Intelligence ,Machine Learning (cs.LG) - 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., Fourth Workshop on Machine Learning and the Physical Sciences at NeurIPS 2021
- Published
- 2021