2 papers
physics.flu-dyn2026
Multi-fidelity graph-based neural networks architectures to learn Navier-Stokes solutions on non-parametrized 2D domains
Francesco Songia, Raoul Sallé de Chou, Hugues Talbot +1
We propose a graph-based, multi-fidelity learning framework for the prediction of stationary Navier--Stokes solutions in non-parametrized two-dimensional geometries. The method is…
math.OC2025
Improved Physics-informed neural networks loss function regularization with a variance-based term
John M. Hanna, Hugues Talbot, Irene E. Vignon-Clementel
In machine learning and statistical modeling, the mean square or absolute error is commonly used as an error metric, also called a "loss function." While effective in reducing the…