17 papers
Alternating Levenberg-Marquardt Training of Physics-Informed Neural Networks with Fourier-Enhanced Features
Yulun Wu, Matthieu Barreau, Miguel Aguiar +1
Physics-informed neural networks (PINNs) often fail to accurately resolve partial differential equations (PDEs) with high-frequency or multi-scale solutions, as well as strongly no…
Hybrid System Identification of Electric Freight Transition Dynamics via SINDy and Neural ODEs
Alessandro Carli, Maret Clement, Jonas MÃ¥rtensson +2
The transition to electric heavy-duty freight is constrained by a strong interdependence between vehicle adoption and charging infrastructure deployment. While system dynamics mode…
Detectability of Subtle Anomalies in Dynamical Systems via Log-Likelihood Ratio
Alejandro Penacho Riveiros, Matthieu Barreau, Nicola Bastianello
Industrial control applications require detecting system anomalies as accurately and quickly as possible to enable prompt maintenance. In this context, it is common to consider sev…
Model-free Anomaly Detection for Dynamical Systems with Gaussian Processes
Alejandro Penacho Riveiros, Nicola Bastianello, Matthieu Barreau
In this paper we address the problem of detecting differences or anomalies in a dynamical system, based on historical data of nominal operations. This problem encompasses quality c…
Physics-Informed Detection of Friction Anomalies in Satellite Reaction Wheels
Alejandro Penacho Riveiros, Nicola Bastianello, Karl H. Johansson +1
As the number of satellites in orbit has increased exponentially in recent years, ensuring their correct functionality has started to require automated methods to decrease human wo…
Second Order Physics-Informed Learning of Road Density using Probe Vehicles
S. Betancur Giraldo, J. MÃ¥rtensson, M. Barreau
We propose a Physics Informed Learning framework for reconstructing traffic density from sparse trajectory data. The approach combines a second-order Aw-Rascle and Zhang model with…