Print Email Facebook Twitter Predicting traction return current in electric railway systems through physics-informed neural networks Title Predicting traction return current in electric railway systems through physics-informed neural networks Author Kapoor, T. (TU Delft Railway Engineering) Wang, H. (TU Delft Railway Engineering) Nunez, Alfredo (TU Delft Railway Engineering) Dollevoet, R.P.B.J. (TU Delft Railway Engineering) Contributor Ishibuchi, Hisao (editor) Kwoh, Chee-Keong (editor) Tan, Ah-Hwee (editor) Srinivasan, Dipti (editor) Miao, Chunyan (editor) Trivedi, Anupam (editor) Crockett, Keeley (editor) Date 2022 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. Subject Traction return currentelectric railway systemsphysics-informed neural networksMonte Carloactivation functions To reference this document use: http://resolver.tudelft.nl/uuid:024b5acb-287e-4fcb-8c5b-a8f8891ed75d DOI https://doi.org/10.1109/SSCI51031.2022.10022290 Publisher IEEE, Piscataway Embargo date 2023-07-30 ISBN 978-1-6654-8769-6 Source Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence (SSCI) Event 2022 IEEE Symposium Series on Computational Intelligence (SSCI), 2022-12-04 → 2022-12-07, Singapore, Singapore Bibliographical note Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. Part of collection Institutional Repository Document type conference paper Rights © 2022 T. Kapoor, H. Wang, Alfredo Nunez, R.P.B.J. Dollevoet Files PDF Predicting_traction_retur ... tworks.pdf 1.03 MB Close viewer /islandora/object/uuid:024b5acb-287e-4fcb-8c5b-a8f8891ed75d/datastream/OBJ/view