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Machine prediction of topological transitions in photonic crystals.
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论文类型：期刊论文
发表刊物：Physical Review Applied
所属单位：光电学院，国家光电研究中心
刊物所在地：美国
学科门类：物理学
项目来源：自然科学基金
文献类型：J
卷号：14
期号：4
页面范围：044032
关键字：无
DOI码：10.1103/PhysRevApplied.14.044032
发表时间：2020-09-08
教研室：c716
摘要：Neural networks based on machine learning can interpolate well within the training dataset, but their ability to extrapolate is severely limited by fundamental issues such as the bias-variance trade-off. Here we introduce the concept of an operator parameter space consisting of physical entities encoded with Maxwell’s equations to improve the networks’ capability to generalize beyond their training set. We illustrate the idea with photonic crystals, and show that the network trained with operator parameters yields remarkably accurate predictions of the topological transitions both within and beyond the training physical space. Such concepts can be generalized to higher-dimensional wave structures by choosing the appropriate operator parameters.
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