Physics-Informed Deep Neural Networks for Transient Electromagnetic Analysis

Oameed Noakoasteen, Shu Wang, Zhen Peng, Christos Christodoulou

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we propose a deep neural network based model to predict the time evolution of field values in transient electrodynamics. The key component of our model is a recurrent neural network, which learns representations of long-term spatial-temporal dependencies in the sequence of its input data. We develop an encoder-recurrent-decoder architecture, which is trained with finite difference time domain simulations of plane wave scattering from distributed, perfect electric conducting objects. We demonstrate that, the trained network can emulate a transient electrodynamics problem with more than 17 times speed-up in simulation time compared to traditional finite difference time domain solvers.

Original languageEnglish (US)
Article number9158400
Pages (from-to)404-412
Number of pages9
JournalIEEE Open Journal of Antennas and Propagation
Volume1
Issue number1
DOIs
StatePublished - 2020

Keywords

  • Computer vision
  • Electromagnetics
  • Finite difference methods
  • Machine learning
  • Recurrent neural networks
  • Unsupervised learning

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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