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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Published in | IEEE Open Journal of Antennas and Propagation Vol. 1; p. 1 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
IEEE
01.01.2020
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Subjects | |
Online Access | Get full text |
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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. |
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ISSN: | 2637-6431 2637-6431 |
DOI: | 10.1109/OJAP.2020.3013830 |