Physics-Informed Deep Neural Networks for Transient Electromagnetic Analysis

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 d...

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Bibliographic Details
Published inIEEE Open Journal of Antennas and Propagation Vol. 1; p. 1
Main Authors Noakoasteen, Oameed, Wang, Shu, Peng, Zhen, Christodoulou, Christos
Format Journal Article
LanguageEnglish
Published IEEE 01.01.2020
Subjects
Computational modeling
Computer vision
electromagnetics
Finite difference methods
machine learning
Mathematical model
Physics
recurrent neural networks
Time-domain analysis
Training
Transient analysis
unsupervised learning
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Summary: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.
ISSN:2637-6431
2637-6431
DOI:10.1109/OJAP.2020.3013830