arXiv Machine Learning July 31, 2026 By Anton Myshak, Md Rezwan Bin Mizan, Ilya Timofeyev

Comparison of a Parametric Physics-Informed Neural Network and a Tensorial Reduced-Order Model for the Shallow-Water Dam-Break Problem

Read the original on arXiv Machine Learning →

arXiv:2607. 27433v1 Announce Type: cross Abstract: We develop two parametric data-driven reduced models: a physics-informed neural network (PINN) and a non-intrusive tensorial reduced-order model (TROM), and apply both approaches to the parametrized one-dimensional shallow-water dam-break problem.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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