3 papers
cs.LG2026
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…
math.NA2026
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…
cs.LG2025
Machine learning in wastewater treatment: insights from modelling a pilot denitrification reactor
Eivind Bøhn, Sølve Eidnes, Kjell Rune Jonassen
Wastewater treatment plants are increasingly recognized as promising candidates for machine learning applications, due to their societal importance and high availability of data. H…