62 citations · 64 across the 7 of their papers we have counts for
8 papers
Simultaneous energy and entropy stability of volume-penalisation immersed-boundary methods for the compressible Navier--Stokes equations
Abbas Ballout, Albert Jiménez-Ramos, Gonzalo Rubio +1
Immersed-boundary methods avoid body-fitted meshes, but the energy or entropy stability of the underlying flow discretisation does not automatically extend to the immersed treatmen…
HORSES3D-GPU: A high-order discontinuous Galerkin solver for multi-GPU systems
Gerasimos Ntoukas, Gonzalo Rubio, Abbas Ballout +13
We present the GPU acceleration and large-scale performance assessment of HORSES3D, an open-source high-order discontinuous Galerkin solver for computational fluid dynamics. The so…
Optimal solutions employing an algebraic Variational Multiscale approach Part II: Application to Navier-Stokes
Suyash Shrestha, Marc Gerritsma, Gonzalo Rubio +2
This work presents a non-linear extension of the high-order discretisation framework based on the Variational Multiscale (VMS) method previously introduced for steady linear proble…
Acoustic Propagation/Refraction Through Diffuse Interface Models
Abbas Ballout, Oscar A. Marino, Gerasimos Ntoukas +2
We present a novel approach for simulating acoustic (pressure) wave propagation across different media separated by a diffuse interface through the use of a weak compressibility fo…
Optimal solutions employing an algebraic Variational Multiscale approach Part I: Steady Linear Problems
Suyash Shrestha, Marc Gerritsma, Gonzalo Rubio +2
This work extends our previous study from S. Shrestha et al. (2024) by introducing a new abstract framework for Variational Multiscale (VMS) methods at the discrete level. We intro…
A reinforcement learning strategy to automate and accelerate h/p-multigrid solvers
David Huergo, Laura Alonso, Saumitra Joshi +3
We explore a reinforcement learning strategy to automate and accelerate h/p-multigrid methods in high-order solvers. Multigrid methods are very efficient but require fine-tuning of…
Reinforcement learning for anisotropic p-adaptation and error estimation in high-order solvers
David Huergo, Martín de Frutos, Eduardo Jané +3
We present a novel approach to automate and optimize anisotropic p-adaptation in high-order h/p solvers using Reinforcement Learning (RL). The dynamic RL adaptation uses the evolvi…
HORSES3D: a high-order discontinuous Galerkin solver for flow simulations and multi-physics applications
E. Ferrer, G. Rubio, G. Ntoukas +10
We present the latest developments of our High-Order Spectral Element Solver (HORSES3D), an open source high-order discontinuous Galerkin framework, capable of solving a variety of…