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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…
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…