Shape Analysis on Lie Groups and Homogeneous Spaces. - Elena Celledoni; Sølve Eidnes; Markus Eslitzbichler; Alexander Schmeding (GSI, 2017)

Adaptive energy preserving methods for partial differential equations. - Sølve Eidnes; Brynjulf Owren; Torbjørn Ringholm (Adv. Comput. Math., 2018)

Dissipative Numerical Schemes on Riemannian Manifolds with Applications to Gradient Flows. - Elena Celledoni; Sølve Eidnes; Brynjulf Owren; Torbjørn Ringholm (SIAM J. Sci. Comput., 2018)

Linearly implicit local and global energy-preserving methods for Hamiltonian PDEs. - Sølve Eidnes; Lu Li (CoRR, 2019)

Order theory for discrete gradient methods. - Sølve Eidnes (CoRR, 2020)

Energy-preserving methods on Riemannian manifolds. - Elena Celledoni; Sølve Eidnes; Brynjulf Owren; Torbjørn Ringholm (Math. Comput., 2020)

Linearly Implicit Local and Global Energy-Preserving Methods for PDEs with a Cubic Hamiltonian. - Sølve Eidnes; Lu Li (SIAM J. Sci. Comput., 2020)

Linearly implicit structure-preserving schemes for Hamiltonian systems. - Sølve Eidnes; Lu Li; Shun Sato 001 (J. Comput. Appl. Math., 2021)

Data quality issues for vibration sensors: a case study in ferrosilicon production. - Maryna Waszak; Terje Moen; Sølve Eidnes; Alexander J. Stasik; Anders H. Hansen; Gregory Bouquet; Antoine Pultier; Xiang Ma; Idar Tørlen; Bjørn Henriksen; Arianeh Aamodt; Dumitru Roman (SEA4DQ@ESEC/SIGSOFT FSE, 2022)

Port-Hamiltonian Neural Networks with State Dependent Ports. - Sølve Eidnes; Alexander J. Stasik; Camilla Sterud; Eivind Bøhn; Signe Riemer-Sørensen (CoRR, 2022)

Pseudo-Hamiltonian system identification. - Sigurd Holmsen; Sølve Eidnes; Signe Riemer-Sørensen (CoRR, 2023)

Learning Dynamical Systems from Noisy Data with Inverse-Explicit Integrators. - Håkon Noren; Sølve Eidnes; Elena Celledoni (CoRR, 2023)

Recency-Weighted Temporally-Segmented Ensemble for Time-Series Modeling. - Pål V. Johnsen; Eivind Bøhn; Sølve Eidnes; Filippo Remonato; Signe Riemer-Sørensen (CoRR, 2024)

Machine learning in wastewater treatment: insights from modelling a pilot denitrification reactor. - Eivind Bøhn; Sølve Eidnes; Kjell Rune Jonassen (CoRR, 2024)

Pseudo-Hamiltonian neural networks for learning partial differential equations. - Sølve Eidnes; Kjetil Olsen Lye (J. Comput. Phys., 2024)