1 citations · 1 across the 15 of their papers we have counts for
18 papers
Learning Traffic Flow Dynamics with Stochastic Physics-Informed Neural Cellular Automata
Federica Bragone, Matthieu Barreau
Traffic flow modeling is essential for understanding and predicting the collective dynamics of vehicles on road networks. Cellular automata provide a simple, interpretable yet powe…
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 Mårtensson +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…
Second Order Physics-Informed Learning of Road Density using Probe Vehicles
S. Betancur Giraldo, J. Mårtensson, 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…
Curriculum-Learned Vanishing Stacked Residual PINNs for Hyperbolic PDE State Reconstruction
Katayoun Eshkofti, Matthieu Barreau
Modeling distributed dynamical systems governed by hyperbolic partial differential equations (PDEs) remains challenging due to discontinuities and shocks that hinder the convergenc…
Modeling and Physics-Enhanced Fault Detection in Wastewater Pump Stations
Katayoun Eshkofti, Henrik Sandberg, Mikael Nilsson +1
Monitoring wastewater pump stations is essential because they are critical infrastructure. However, monitoring is still often performed manually due to the lack of suitable algorit…
Iterative Training of Physics-Informed Neural Networks with Fourier-enhanced Features
Yulun Wu, Miguel Aguiar, Karl H. Johansson +1
Spectral bias, the tendency of neural networks to learn low-frequency features first, is a well-known issue with many training algorithms for physics-informed neural networks (PINN…
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…
Source-Guided Flow Matching
Zifan Wang, Alice Harting, Matthieu Barreau +2
Guidance of generative models is typically achieved by modifying the probability flow vector field through the addition of a guidance field. In this paper, we instead propose the S…
Data-driven multi-agent modelling of calcium interactions in cell culture: PINN vs Regularized Least-squares
Aurora Poggi, Giuseppe Alessio D'Inverno, Hjalmar Brismar +3
Data-driven discovery of dynamics in biological systems allows for better observation and characterization of processes, such as calcium signaling in cell culture. Recent advanceme…
Online Traffic Density Estimation using Physics-Informed Neural Networks
Dennis Wilkman, Kateryna Morozovska, Karl Henrik Johansson +1
Recent works on the application of Physics-Informed Neural Networks to traffic density estimation have shown to be promising for future developments due to their robustness to mode…
Closed-Loop Neural Operator-Based Observer of Traffic Density
Alice Harting, Karl Henrik Johansson, Matthieu Barreau
We consider the problem of traffic density estimation with sparse measurements from stationary roadside sensors. Our approach uses Fourier neural operators to learn macroscopic tra…
Optimal Sensor Placement in Power Transformers Using Physics-Informed Neural Networks
Sirui Li, Federica Bragone, Matthieu Barreau +2
Our work aims at simulating and predicting the temperature conditions inside a power transformer using Physics-Informed Neural Networks (PINNs). The predictions obtained are then u…
Data-Driven vs Traditional Approaches to Power Transformer's Top-Oil Temperature Estimation
Francis Tembo, Federica Bragone, Tor Laneryd +2
Power transformers are subjected to electrical currents and temperature fluctuations that, if not properly controlled, can lead to major deterioration of their insulation system. T…
MILP initialization for solving parabolic PDEs with PINNs
Sirui Li, Federica Bragone, Matthieu Barreau +1
Physics-Informed Neural Networks (PINNs) are a powerful deep learning method capable of providing solutions and parameter estimations of physical systems. Given the complexity of t…
A Control Perspective on Training PINNs
Matthieu Barreau, Haoming Shen
We investigate the training of Physics-Informed Neural Networks (PINNs) from a control-theoretic perspective. Using gradient descent with resampling, we interpret the training dyna…