Physics-Informed Deep Neural Networks for Transient Electromagnetic Analysis

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
Main Authors: Oameed Noakoasteen, Shu Wang, Zhen Peng, Christos Christodoulou
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Open Journal of Antennas and Propagation
Subjects:
Computer vision
electromagnetics
finite difference methods
machine learning
recurrent neural networks
unsupervised learning
Online Access:https://ieeexplore.ieee.org/document/9158400/

Internet

https://ieeexplore.ieee.org/document/9158400/

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