Physics-Informed Learning Using Hamiltonian Neural Networks with Output Error Noise Models. - Sarvin Moradi; Nick O. Jaensson; Roland Tóth; Maarten Schoukens (CoRR, 2023)

Learning Subsystem Dynamics in Nonlinear Systems via Port-Hamiltonian Neural Networks. - G. J. E. van Otterdijk; Sarvin Moradi; Siep Weiland; Roland Tóth; Nick O. Jaensson; Maarten Schoukens (CDC, 2025)

Port-Hamiltonian neural networks with output error noise models. - Sarvin Moradi; Gerben Izaak Beintema; Nick O. Jaensson; Roland Tóth; Maarten Schoukens (Autom., 2026)

Bayesian Model Selection for Complex Flows of Yield Stress Fluids. - Aricia Rinkens; Clemens V. Verhoosel; Alexandra Alicke; Patrick D. Anderson; Nick O. Jaensson (CoRR, 2026)

Identification of Port-Hamiltonian Differential-Algebraic Equations from Input-Output Data. - N. Hagelaars; G. J. E. van Otterdijk; Sarvin Moradi; Roland Tóth; Nick O. Jaensson; Maarten Schoukens (CoRR, 2026)

A comparison of Markov Chain Monte Carlo algorithms for Bayesian inference of constitutive models. - Aricia Rinkens; Rodrigo L. S. Silva; Erik Quaeghebeur; Nick O. Jaensson; Clemens Verhoosel (CoRR, 2026)