Ren Simiao, Mahendra Ashwin, Khatib Omar, Deng Yang, Padilla Willie J, Malof Jordan M
Department of Electrical and Computer Engineering, Duke University, Box 90291, Durham, NC 27708, USA.
Nanoscale. 2022 Mar 10;14(10):3958-3969. doi: 10.1039/d1nr08346e.
In this work we investigate the use of deep inverse models (DIMs) for designing artificial electromagnetic materials (AEMs) - such as metamaterials, photonic crystals, and plasmonics - to achieve some desired scattering properties (, transmission or reflection spectrum). DIMs are deep neural networks (, deep learning models) that are specially-designed to solve ill-posed inverse problems. There has recently been tremendous growth in the use of DIMs for solving AEM design problems however there has been little comparison of these approaches to examine their absolute and relative performance capabilities. In this work we compare eight state-of-the-art DIMs on three unique AEM design problems, including two models that are novel to the AEM community. Our results indicate that DIMs can rapidly produce accurate designs to achieve a custom desired scattering on all three problems. Although no single model always performs best, the Neural-Adjoint approach achieves the best overall performance across all problem settings. As a final contribution we show that not all AEM design problems are ill-posed, and in such cases a conventional deep neural network can perform better than DIMs. We recommend that a deep neural network is always employed as a simple baseline approach when addressing AEM design problems. We publish python code for our AEM simulators and our DIMs to enable easy replication of our results, and benchmarking of new DIMs by the AEM community.
在这项工作中，我们研究了深度逆模型（DIM）在设计人工电磁材料（AEM）中的应用，这些材料包括超材料、光子晶体和等离子体，以实现某些所需的散射特性（如透射或反射光谱）。DIM是专门设计用于解决不适定逆问题的深度神经网络（即深度学习模型）。最近，DIM在解决AEM设计问题方面的应用有了巨大增长，然而，对这些方法进行比较以检验其绝对和相对性能的研究却很少。在这项工作中，我们在三个独特的AEM设计问题上比较了八种最先进的DIM，其中包括两种对AEM领域来说新颖的模型。我们的结果表明，DIM能够快速生成精确的设计，以在所有这三个问题上实现定制的所需散射。虽然没有一个单一模型总是表现最佳，但神经伴随方法在所有问题设置中实现了最佳的整体性能。作为最后的贡献，我们表明并非所有AEM设计问题都是不适定的，在这种情况下，传统的深度神经网络可以比DIM表现得更好。我们建议在解决AEM设计问题时，始终将深度神经网络作为一种简单的基线方法来使用。我们发布了用于我们的AEM模拟器和DIM的Python代码，以便能够轻松复制我们的结果，并供AEM社区对新的DIM进行基准测试。
