62 citations · 65 across the 11 of their papers we have counts for
4 papers · 1 filter
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…
Can Explicit Subgrid Models Enhance Implicit LES Simulations? A Very High-Order Solver Perspective
Gonzalo Rubio, Gerasimos Ntoukas, Miguel Chávez-Módena +5
High-order discontinuous Galerkin (DG) methods offer excellent accuracy for turbulent-flow simulations and are increasingly attractive on GPU-oriented architectures, where high pol…
A comparison of h- and p-refinement to capture wind turbine wakes
Hatem Kessasra, Marta Cordero-Gracia, Mariola Gómez +3
This paper investigates a critical aspect of wind energy research - the development of wind turbine wake and its significant impact on wind farm efficiency. The study focuses on th…
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…