Deep Learning for the modeling and design of artificial electromagnetic materials

Artificial electromagnetic materials (AEMs) are materials that exhibit unusual electromagnetic properties. With sub-wavelength, periodic structures, AEMs can achieve incredible abilities to manipulate light, like the cloaking effect of the “invisibility cloak” in the Harry Potter movie. Apart from the cinematic application of invisibility, AEMs have important applications ranging from high-efficiency solar panels to next-generation communications systems. The major goal of this thesis is to develop deep learning tools to design materials that have increasingly customized interactions with electromagnetic waves, thus enabling more useful technologies. In turn, this necessitates the modeling and design of increasingly complicated materials. Modeling of these materials is difficult because (i) the physics of advanced materials is intrinsically more complicated with no simple analytical form, (ii) the manufacture of such nano-structures is prohibitively expensive, and (iii) the computational electromagnetic simulation software is too slow to iterative through trail-n-error. Recently, the advancement of deep learning bring new perspectives on such a problem. In this thesis, we explore deep learning for the modeling and design of advanced photonic materials. In particular, we explore and make important contributions to two fundamental areas: inverse design, and active learning. In inverse design, we develop an accurate method, “Neural-adjoint,” and show its dominance not only in simple inverse problems but also in contemporary AEM design problems. We further analyze and benchmark eight state-of-the-art deep inverse approaches in the AEM inverse design and discover that the one-to-manyness of the problem is an important factor in such a problem. Then, motivated by the immediate drawback that all deep inverse models require a large set of labeled data, we investigate the benefit of active learning in the setting of AEM design and scientific computing in general. By setting the problem close to a real application where pool size is unknown, we find the majority of deep regression pool-based active learning methods in our benchmark lack robustness and don’t outperform even random sampling consistently.