arXiv Machine Learning August 4, 2026 By Enzo Nicolas Spotorno, Josafat Leal Filho

An Embedded RISC-V Evaluation of Kolmogorov--Arnold Networks in Hard-Constrained Recurrent Physics-Informed Models

Read the original on arXiv Machine Learning →

arXiv:2608. 00737v1 Announce Type: new Abstract: Hard-constrained recurrent physics-informed networks (HRPINNs) embed known dynamics inside a recurrent numerical integrator and restrict a neural branch to learning only the residual dynamics that the first-principles model does not capture.

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

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