深度学习
人工神经网络
反问题
电介质
反向
领域(数学)
软件
人工智能
实施
计算机工程
计算机科学
物理
数学
几何学
光电子学
数学分析
程序设计语言
纯数学
作者
Christian C. Nadell,Bo-Wun Huang,Jordan M. Malof,Willie J. Padilla
出处
期刊:Optics Express
[The Optical Society]
日期:2019-09-16
卷期号:27 (20): 27523-27523
被引量:272
摘要
Deep learning has risen to the forefront of many fields in recent years, overcoming challenges previously considered intractable with conventional means. Materials discovery and optimization is one such field, but significant challenges remain, including the requirement of large labeled datasets and one-to-many mapping that arises in solving the inverse problem. Here we demonstrate modeling of complex all-dielectric metasurface systems with deep neural networks, using both the metasurface geometry and knowledge of the underlying physics as inputs. Our deep learning network is highly accurate, achieving an average mean square error of only 1.16 × 10-3 and is over five orders of magnitude faster than conventional electromagnetic simulation software. We further develop a novel method to solve the inverse modeling problem, termed fast forward dictionary search (FFDS), which offers tremendous controls to the designer and only requires an accurate forward neural network model. These techniques significantly increase the viability of more complex all-dielectric metasurface designs and provide opportunities for the future of tailored light matter interactions.
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