Manifold Learning for Knowledge Discovery and Intelligent Inverse Design of Photonic Nanostructures: Breaking the Geometric Complexity

Autor:	Zandehshahvar, Mohammadreza, Kiarashi, Yashar, Zhu, Muliang, Maleki, Hossein, Brown, Tyler, Adibi, Ali
Rok vydání:	2021
Předmět:	Physics - Optics Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Here, we present a new approach based on manifold learning for knowledge discovery and inverse design with minimal complexity in photonic nanostructures. Our approach builds on studying sub-manifolds of responses of a class of nanostructures with different design complexities in the latent space to obtain valuable insight about the physics of device operation to guide a more intelligent design. In contrast to the current methods for inverse design of photonic nanostructures, which are limited to pre-selected and usually over-complex structures, we show that our method allows evolution from an initial design towards the simplest structure while solving the inverse problem. Comment: 10 pages, 6 figures, 2 tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2102.04454 Zobrazit plný text záznamu View this record from Arxiv