Physics-informed neural network for predicting electric field distributions and permittivities of circular split-ring resonators

Prajith P. Pillai; Anirban Chaudhuri; Beena Rai; Parama Pal

doi:10.1117/12.2609446

7 March 2022 Physics-informed neural network for predicting electric field distributions and permittivities of circular split-ring resonators

Prajith P. Pillai, Anirban Chaudhuri, Beena Rai, Parama Pal

Author Affiliations +

Proceedings Volume 12019, AI and Optical Data Sciences III; 120190R (2022) https://doi.org/10.1117/12.2609446
Event: SPIE OPTO, 2022, San Francisco, California, United States

Conference Poster

Abstract

Physics informed neural networks (PINNs) solve supervised learning tasks by incorporating partial differential equations describing the governing physics. We use a PINN based on Maxwell’s equations in the frequency domain to predict the electrical permittivity parameters, and hence the electric fields, of circular split-ring resonator-based metamaterials thereby bypassing full-wave solutions based on finite-element methods. We demonstrate the use of a PINN for the inverse prediction of the electrical permittivity of a circular split ring resonator metamaterial given the spatial e-field distributions at the resonant frequency. Our results validate the PINN framework for the inverse retrieval of permittivities from field distributions.

Citation Download Citation

Prajith P. Pillai, Anirban Chaudhuri, Beena Rai, and Parama Pal "Physics-informed neural network for predicting electric field distributions and permittivities of circular split-ring resonators", Proc. SPIE 12019, AI and Optical Data Sciences III, 120190R (7 March 2022); https://doi.org/10.1117/12.2609446

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available