2 citations · 2 across the 8 of their papers we have counts for
8 papers
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…
A Certifiable Machine Learning-Based Pipeline to Predict Fatigue Life of Aircraft Structures
Ángel Ladrón, Miguel Sánchez-Domínguez, Javier Rozalén +5
Fatigue life prediction is essential in both the design and operational phases of any aircraft, and in this sense safety in the aerospace industry requires early detection of fatig…
Transfer learning-enhanced deep reinforcement learning for aerodynamic airfoil optimisation subject to structural constraints
David Ramos, Lucas Lacasa, Eusebio Valero +1
The main objective of this paper is to introduce a transfer learning-enhanced deep reinforcement learning (DRL) methodology that is able to optimise the geometry of any airfoil bas…
Towards certification: A complete statistical validation pipeline for supervised learning in industry
Lucas Lacasa, Abel Pardo, Pablo Arbelo +8
Methods of Machine and Deep Learning are gradually being integrated into industrial operations, albeit at different speeds for different types of industries. The aerospace and aero…