通过复合贝叶斯优化来优化高维物理模拟

基于物理模拟的优化是科学和工程领域的一项常见任务。许多这样的模拟产生了基于图像或张量的输出，其中所需的目标是这些输出的函数，并且优化是在高维参数空间上进行的。我们开发了一种贝叶斯优化方法，利用基于张量的高斯过程代用品和信任区贝叶斯优化来有效地模拟图像输出，并有效地优化这些类型的模拟，包括一个射频塔配置问题和一个光学设计问题。

原文题目：Optimizing High-Dimensional Physics Simulations via Composite Bayesian Optimization

原文：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.