22 citations · 45 across the 14 of their papers we have counts for
5 papers · 1 filter
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…
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…
Learning Dynamical Systems from Noisy Data with Inverse-Explicit Integrators
Håkon Noren, Sølve Eidnes, Elena Celledoni
We introduce the mean inverse integrator (MII), a novel approach to increase the accuracy when training neural networks to approximate vector fields of dynamical systems from noisy…
Pseudo-Hamiltonian neural networks for learning partial differential equations
Sølve Eidnes, Kjetil Olsen Lye
Pseudo-Hamiltonian neural networks (PHNN) were recently introduced for learning dynamical systems that can be modelled by ordinary differential equations. In this paper, we extend…
Pseudo-Hamiltonian Neural Networks with State-Dependent External Forces
Sølve Eidnes, Alexander J. Stasik, Camilla Sterud +2
Hybrid machine learning based on Hamiltonian formulations has recently been successfully demonstrated for simple mechanical systems, both energy conserving and not energy conservin…