6 citations · 9 across the 9 of their papers we have counts for
3 papers · 1 filter
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…