62 citations · 65 across the 12 of their papers we have counts for
12 papers
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…
Kolmogorov Arnold networks (KAN) for aerodynamic prediction: a comparison with MLPs and GNNs
Miguel Jaraiz, Fermin Gutierrez, Pablo Yeste +4
Kolmogorov Arnold networks (KAN) have recently been introduced as a (deep) neural network architecture whose trainable parameters adapt the activation functions, instead of the coe…
FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes
David Ramos, Lucas Lacasa, Fermín Gutiérrez +2
Computational fluid dynamics (CFD) provides high-fidelity simulations of fluid flows but remains computationally expensive for many-query applications. In recent years deep learnin…
On the Role of Consistency Between Physics and Data in Physics-Informed Neural Networks
Nicolás Becerra-Zuniga, Lucas Lacasa, Eusebio Valero +1
Physics-informed neural networks (PINNs) have gained significant attention as a surrogate modeling strategy for partial differential equations (PDEs), particularly in regimes where…
Reliable Statistical Guarantees for Conformal Predictors with Small Datasets
Miguel Sánchez-Domínguez, Lucas Lacasa, Javier de Vicente +2
Surrogate models (including deep neural networks and other machine learning algorithms in supervised learning) are capable of approximating arbitrarily complex, high-dimensional in…
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…