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On the Limitations of Physics-informed Deep Learning: Illustrations
Using First Order Hyperbolic Conservation Law-based Traffic Flow Models

On the Limitations of Physics-informed Deep Learning: Illustrations Using First Order Hyperbolic Conservation Law-based Traffic Flow Models

IEEE Open Journal of Intelligent Transportation Systems (JOITS), 2023

23 February 2023

Archie J. Huang

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Papers citing "On the Limitations of Physics-informed Deep Learning: Illustrations Using First Order Hyperbolic Conservation Law-based Traffic Flow Models"

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Challenges and Advancements in Modeling Shock Fronts with Physics-Informed Neural Networks: A Review and Benchmarking Study J. Abbasi Ameya D. Jagtap Ben Moseley Aksel Hiorth P. Andersen PINN AI4CE 171 6 0 14 Mar 2025
Projected Neural Differential Equations for Learning Constrained Dynamics Alistair J R White Anna Buttner Maximilian Gelbrecht Valentin Duruisseaux Niki Kilbertus Frank Hellmann Niklas Boers 276 2 0 31 Oct 2024
Science based AI model certification for new operational environments with application in traffic state estimationIEEE International Conference on Electro/Information Technology (EIT), 2024 Daryl Mupupuni Anupama Guntu Liang Hong Kamrul Hasan Leehyun Keel 120 0 0 13 May 2024
Capturing Shock Waves by Relaxation Neural Networks Nan Zhou Zheng Ma PINN 111 3 0 01 Apr 2024
Operator learning for hyperbolic partial differential equations Christopher Wang Alex Townsend 265 5 0 29 Dec 2023
Incorporating Nonlocal Traffic Flow Model in Physics-informed Neural Networks Archie J. Huang A. Biswas S. Agarwal 174 6 0 22 Aug 2023
Fourier neural operator for learning solutions to macroscopic traffic flow models: Application to the forward and inverse problemsTransportation Research Part C: Emerging Technologies (TRC), 2023 Bilal Thonnam Thodi Sai Venkata Ramana Ambadipudi Saif Eddin Jabari AI4CE 292 17 0 14 Aug 2023