22 citations · 45 across the 14 of their papers we have counts for
3 papers · 1 filter
SIPHy: Sparse identification of port-Hamiltonian systems from noisy data
Håkon Noren Myhr, Sølve Eidnes, J. Nathan Kutz
We propose sparse identification of port-Hamiltonian systems (SIPHy), enabling structure-preserving symbolic regression from noisy trajectory observations. The method applies to po…
Learning partially observed systems with neural Hamiltonian ordinary differential equations
Sunniva Meltzer, Sølve Eidnes, Alexander Johannes Stasik
When learning dynamical systems from data, embedding physical structure can constrain the solution space and improve generalization, but many physics-informed models assume access…
Derivative-free discrete gradient methods
Håkon Noren Myhr, Sølve Eidnes
Discrete gradient methods are a class of numerical integrators producing solutions with exact preservation of first integrals of ordinary differential equations. In this paper, we…