6 citations · 7 across the 3 of their papers we have counts for
3 papers
A Gaussian-based spatially weighted loss formulation for Physics-Informed Neural Networks with Shocks
Kheir-eddine Otmani, Abdelhalim Azzouz, Nourelhouda Groun +1
Physics-informed neural networks (PINNs) often struggle to resolve shocks because smooth neural representations and globally averaged residual losses tend to underemphasize localiz…
Accelerating high order discontinuous Galerkin solvers through a clustering-based viscous/turbulent-inviscid domain decomposition
Kheir-Eddine Otmani, Andrés Mateo-Gabín, Gonzalo Rubio +1
We explore the unsupervised clustering technique introduced in [25] to identify viscous/turbulent from inviscid regions in incompressible flows. The separation of regions allows so…
Machine learning adaptation for laminar and turbulent flows: applications to high order discontinuous Galerkin solvers
Kenza Tlales, Kheir-Eddine Otmani, Gerasimos Ntoukas +2
We present a machine learning-based mesh refinement technique for steady and unsteady flows. The clustering technique proposed by Otmani et al. arXiv:2207.02929 [physics.flu-dyn] i…