22 citations · 23 across the 8 of their papers we have counts for
5 papers · 1 filter
Virtual Temperature Sensors in Power Transformers Using Neural Ordinary Differential Equations
Berk Hadzhamolla, Alexander Johannes Stasik, Signe Riemer-Sørensen
Accurate modeling and forecasting of power transformer thermal behavior are critical for reliability, asset lifetime, and optimized power system operation. Numerical approaches suc…
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…
Fast Bayesian equipment condition monitoring via simulation based inference: applications to heat exchanger health
Peter Collett, Alexander Johannes Stasik, Simone Casolo +1
Accurate condition monitoring of industrial equipment requires inferring latent degradation parameters from indirect sensor measurements under uncertainty. While traditional Bayesi…
Unreliable Uncertainty Estimates with Monte Carlo Dropout
Aslak Djupskås, Alexander Johannes Stasik, Signe Riemer-Sørensen
Reliable uncertainty estimation is crucial for machine learning models, especially in safety-critical domains. While exact Bayesian inference offers a principled approach, it is of…
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…