22 citations · 45 across the 14 of their papers we have counts for
3 papers · 1 filter
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 system identification
Sigurd Holmsen, Sølve Eidnes, Signe Riemer-Sørensen
Identifying the underlying dynamics of physical systems can be challenging when only provided with observational data. In this work, we consider systems that can be modelled as fir…
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…