Physics-informed neural networks for modelling anisotropic and bi-anisotropic electromagnetic constitutive laws through indirect data-Reference-Cited by-同舟云学术

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Physics-informed neural networks for modelling anisotropic and bi-anisotropic electromagnetic constitutive laws through indirect data

Published:2022-12-04 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE Symposium Series on Computational Intelligence (SSCI)
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Author:

Chandra Abhishek¹,Curti Mitrofan¹,Tiels Koen²,Lomonova Elena A.¹,Tartakovsky Daniel M.³

Affiliation:

1. Eindhoven University of Technology,Department of Electrical Engineering,The Netherlands

2. Eindhoven University of Technology,Department of Mechanical Engineering,The Netherlands

3. Stanford University,Department of Energy Resources Engineering,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10022049/10022014/10022292.pdf?arnumber=10022292

Reference39 articles.

1. Three Benchmarks Addressing Open Challenges in Nonlinear System Identification * *We thank Torbjorn Wigren and Per Mattsson (Uppsala University, Sweden) for their help in realizing the cascaded tanks benchmark. This work was funded by the Fund for Scientific Research (FWO), the Methusalem grant of the Flemish Government (METH-1), the IAP VII/19 DYSCO program, and the ERC advanced grant SNLSID under contract 320378. The author J.P. Noel is a Postdoctoral Researcher of the Fonds de la Recherche Scientifique - FNRS which is gratefully acknowledged.

2. Maxwell’s equations in inhomogeneous bi-anisotropic materials: Existence, uniqueness and stability for the initial value problem

3. Unsupervised discovery of interpretable hyperelastic constitutive laws

4. Multiferroic and magnetoelectric materials

5. Predictive modeling with learned constitutive laws from indirect observations;huang;ArXiv Preprint,2019

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Transfer learning‐based physics‐informed neural networks for magnetostatic field simulation with domain variations;International Journal of Numerical Modelling: Electronic Networks, Devices and Fields;2024-07

2. Physics-Informed Neural Networks for Solving Forward and Inverse Problems in Complex Beam Systems;IEEE Transactions on Neural Networks and Learning Systems;2023

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