2 citations · 2 across the 8 of their papers we have counts for
5 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…
RHYME-XT: A Neural Operator for Spatiotemporal Control Systems
Marijn Ruiter, Miguel Aguiar, Jake Rap +2
We propose RHYME-XT, an operator-learning framework for surrogate modeling of spatiotemporal control systems governed by input-affine nonlinear partial integro-differential equatio…
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…