Bayesian Optimization for Materials Design
Bayesian Optimization for Materials Design
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DOI：
10.1007/978-3-319-23871-5_3
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发表时间：
2016-01-01
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影响因子：
--
通讯作者：
Wang, Jialei
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文献类型：
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作者：
Frazier, Peter I.;Wang, Jialei
我们介绍了贝叶斯优化，这是一种为优化耗时的工程模拟和在大型数据集上拟合机器学习模型而开发的技术。贝叶斯优化指导材料设计和发现过程中实验的选择，在尽可能少的实验中找到好的材料设计。我们关注的是材料设计由低维矢量参数化的情况。贝叶斯优化是建立在一种叫做高斯过程回归的统计技术上的，它可以根据以前测试过的设计来预测新设计的性能。在详细介绍高斯过程回归之后，我们描述了两种贝叶斯优化方法：期望改进，用于无噪声评估的设计问题；知识梯度法泛化了期望改进，可用于有噪声评价的设计问题。这两种方法都是使用信息价值分析得出的，并享有一步贝叶斯最优性。
We introduce Bayesian optimization, a technique developed for optimizing time-consuming engineering simulations and for fitting machine learning models on large datasets. Bayesian optimization guides the choice of experiments during materials design and discovery to find good material designs in as few experiments as possible. We focus on the case when materials designs are parameterized by a low-dimensional vector. Bayesian optimization is built on a statistical technique called Gaussian process regression, which allows predicting the performance of a new design based on previously tested designs. After providing a detailed introduction to Gaussian process regression, we describe two Bayesian optimization methods: expected improvement, for design problems with noise-free evaluations; and the knowledge-gradient method, which generalizes expected improvement and may be used in design problems with noisy evaluations. Both methods are derived using a value-of-information analysis, and enjoy one-step Bayes-optimality.