Predicting traction return current in electric railway systems through physics-informed neural networks

Conference paper (2022)

Authors

T. Kapoor Railway Engineering - CEG
H. Wang Railway Engineering - CEG
Alfredo Nunez Railway Engineering - CEG
R.P.B.J. Dollevoet Railway Engineering - CEG
Research Group
Railway Engineering (CEG) (TU Delft)
Copyright: © 2022 T. Kapoor, H. Wang, Alfredo Nunez, R.P.B.J. Dollevoet
DOI
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https://doi.org/10.1109/SSCI51031.2022.10022290
Monte Carlo Physics-informed neural networks Activation functions Traction return current Electric railway systems
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Published Date

2022

Language

English

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Faculty

Civil Engineering & Geosciences

Department

Engineering Structures

Research Group

Railway Engineering

Abstract

This paper addresses the problem of determining the distribution of the return current in electric railway traction systems. The dynamics of traction return current are simulated in all three space dimensions by informing the neural networks with the Partial Differential Equations (PDEs) known as telegraph equations. In addition, this work proposes a method of choosing optimal activation functions for training the physics-informed neural network to solve higher-dimensional PDEs. We propose a Monte Carlo based framework to choose the activation function in lower dimensions, mitigating the need for ensemble training in higher dimensions. To further strengthen the applicability of the Monte Carlo based framework, experiments are presented under two loss functions governed by L2 and L∞ norms. The presented method efficiently simulates the traction return current for electric railway systems, even for three-dimensional problems.