反向
深度学习
反问题
计算机科学
深层神经网络
人工神经网络
人工智能
领域(数学)
过程(计算)
数学
几何学
操作系统
数学分析
纯数学
作者
Yang Deng,Simiao Ren,Jordan Malof,Willie J. Padilla
标识
DOI:10.1016/j.photonics.2022.101070
摘要
Deep learning (DL) has been used to design deep neural networks (DNNs) which have recently been applied to solving inverse problems in artificial electromagnetic materials (AEMs). Although inverse problems are often ill-posed, and therefore are difficult to solve, deep inverse models (DIMs) have achieved impressive results often surpassing capabilities possible with other approaches. We overview the process of deep inverse learning applied to AEM problems, including the building of data sets, design of a forward model, and comparison of inverse approaches including limitations. We conclude by detailing some important outstanding issues of deep inverse design of AEMs, and present an outlook of this exciting field.
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