8 papers · 1 filter
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…
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…
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…
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…
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…