collaborators

2 papers

cs.LG2026

Multi-fidelity aerodynamic data fusion by autoencoder transfer learning

Javier Nieto-Centenero, Esther Andrés, Rodrigo Castellanos

Accurate aerodynamic prediction often relies on high-fidelity simulations; however, their prohibitive computational costs severely limit their applicability in data-driven modeling…

cs.LG2024

Towards aerodynamic surrogate modeling based on -variational autoencoders

Víctor Francés-Belda, Alberto Solera-Rico, Javier Nieto-Centenero +3

Surrogate models that combine dimensionality reduction and regression techniques are essential to reduce the need for costly high-fidelity computational fluid dynamics data. New ap…