Journal Article10.1109/usnc-ursi52151.2023.10237743
2D Eigenmode Analysis Based on Physics Informed Neural Networks
Md. Rayhan Khan,Constantinos L. Zekios,Shubhendu Bhardwaj,Stavros V. Georgakopoulos +3 more
- 23 Jul 2023
pp 1015-1016
TL;DR: A novel deep learning approach for identifying eigenmode distributions of closed waveguides based on physics informed neural networks is presented. The approach is capable of identifying all the eigenmode distributions with an error of less than −12 dB.
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Abstract: In this work, a novel deep learning-based approach for identifying the modal field distributions of closed waveguides is introduced. Specifically, physics informed neural networks are used to solve the Helmholtz partial differential equation (PDE) with the imposition of the appropriate boundary conditions. Based on our results, the proposed approach is capable of identifying all the eigenmode distributions of the studied waveguides with an error of less than −12 dB when compared with both analytical and full-wave simulation results.
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TL;DR: It is argued that contextual cues should be used as part of deep learning to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales.