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arXiv AI By Achraf El Messaoudi (UMLP, ENSMM, FEMTO-ST), Karim Cherifi (UMLP, ENSMM, FEMTO-ST), Yann Le Gorrec (UMLP, ENSMM, FEMTO-ST), Yongxin Wu (UMLP, ENSMM, FEMTO-ST)

PH-KAN: Port-Hamiltonian Kolmogorov-Arnold Network

Read the original on arXiv AI →

arXiv:2606. 14708v1 Announce Type: cross Abstract: Data-driven machine learning approaches have become increasingly attractive for nonlinear system identification, but standard models often fail to preserve the underlying physical structure and remain difficult to interpret, especially when no analytical model is available.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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