10 citations · 13 across the 8 of their papers we have counts for
3 papers · 1 filter
Fixed-budget online adaptive learning for physics-informed neural networks. Towards parameterized problem inference
Thi Nguyen Khoa Nguyen, Thibault Dairay, Raphaël Meunier +2
Physics-Informed Neural Networks (PINNs) have gained much attention in various fields of engineering thanks to their capability of incorporating physical laws into the models. PINN…
Continuous Methods : Adaptively intrusive reduced order model closure
Emmanuel Menier, Michele Alessandro Bucci, Mouadh Yagoubi +4
Reduced order modeling methods are often used as a mean to reduce simulation costs in industrial applications. Despite their computational advantages, reduced order models (ROMs) o…
Physics-informed neural networks for non-Newtonian fluid thermo-mechanical problems: an application to rubber calendering process
Thi Nguyen Khoa Nguyen, Thibault Dairay, Raphaël Meunier +1
Physics-Informed Neural Networks (PINNs) have gained much attention in various fields of engineering thanks to their capability of incorporating physical laws into the models. Howe…