2 papers
cs.LG2025
Méthode de quadrature pour les PINNs fondée théoriquement sur la hessienne des résiduels
Antoine Caradot, Rémi Emonet, Amaury Habrard +2
Physics-informed Neural Networks (PINNs) have emerged as an efficient way to learn surrogate neural solvers of PDEs by embedding the physical model in the loss function and minimiz…
cs.LG2025
Provably Accurate Adaptive Sampling for Collocation Points in Physics-informed Neural Networks
Antoine Caradot, Rémi Emonet, Amaury Habrard +2
Despite considerable scientific advances in numerical simulation, efficiently solving PDEs remains a complex and often expensive problem. Physics-informed Neural Networks (PINN) ha…