Work

Deep Physical Informed Neural Networks for Metamaterial Design

Year: 2019

Type: article

Abstract: In this paper, we propose a physical informed neural network approach for designing the electromagnetic metamaterial. The approach can be used to deal with various practical problems such as cloaking, rotators, concentrators, etc. The advantage of this approach is the flexibility that we can deal with not only the continuous parameters but also the piecewise constants. As our best knowledge, there is no other faster and much efficient method to deal with these problems. As a byproduct, we propose a method to solve high frequency Helmholtz equation, which is widely used in physics and engineering. Some benchmark problems have been solved in numerical tests to verify our method.

Source: IEEE Access

DOI: 10.1109/access.2019.2963375

Authors: Zhiwei Fang, Justin Zhan

Institutions: University of Nevada, Las Vegas, University of Arkansas at Fayetteville

Language: English

Topic: Metamaterials and Metasurfaces Applications

Subfield: Electronic, Optical and Magnetic Materials

Field: Materials Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,750

Funders: U.S. Department of Defense, Arkansas Research Alliance