2 papers
cs.LG2025
Residual-Informed Learning of Solutions to Algebraic Loops
Felix Brandt, Andreas Heuermann, Philip Hannebohm +1
This paper presents a residual-informed machine learning approach for replacing algebraic loops in equation-based Modelica models with neural network surrogates. A feedforward neur…
cs.LG2025
Efficient Training of Physics-enhanced Neural ODEs via Direct Collocation and Nonlinear Programming
Linus Langenkamp, Philip Hannebohm, Bernhard Bachmann
We propose a novel approach for training Physics-enhanced Neural ODEs (PeN-ODEs) by expressing the training process as a dynamic optimization problem. The full model, including neu…