Predicting Macro Basis Functions for Method of Moments Scattering Problems Using Deep Neural Networks

In this letter, we present research on the application of deep neural networks to predicting macro basis functions for complicated computational electromagnetics problems. We provide error statistics and representative examples for networks trained on simple and complicated datasets of method of mom...

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Bibliographic Details
Published in:IEEE antennas and wireless propagation letters 2021-07, Vol.20 (7), p.1200-1204
Main Authors: Key, Cam, Notaros, Branislav
Format: Article
Language:English
Subjects:
Artificial neural networks
Basis functions
Computational electromagnetics
Computational electromagnetics (CEM)
Datasets
Error analysis
Finite element analysis
machine learning
macro basis functions
Method of moments
method of moments (MoM)
Neural networks
Predictive models
Scattering
Training
variational methods
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Summary:In this letter, we present research on the application of deep neural networks to predicting macro basis functions for complicated computational electromagnetics problems. We provide error statistics and representative examples for networks trained on simple and complicated datasets of method of moments scattering problems. Notably, we demonstrate that the networks learn generalizable knowledge applicable to problem types on which they were not trained. We conclude that the networks produce encouraging results, especially for cross validation, and larger training datasets will improve reliability for general scattering problems.
ISSN:1536-1225
1548-5757
DOI:10.1109/LAWP.2021.3075370