2 citations · 2 across the 8 of their papers we have counts for
5 papers · 1 filter
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…
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…