activity
20242026
most citedTowards aerodynamic surrogate modeling based on -variational autoencoders

16 citations · 16 across the 5 of their papers we have counts for

collaborators

5 papers

physics.flu-dyn2026

Signal-Aware Conditional Diffusion Surrogates for Transonic Wing Pressure Prediction

Víctor Francés-Belda, Carlos Sanmiguel Vila, Rodrigo Castellanos

Accurate and efficient surrogate models for aerodynamic surface pressure fields are essential for accelerating aircraft design and analysis, yet deterministic regressors trained wi…

physics.flu-dyn2026

Optimization-Embedded Active Multi-Fidelity Surrogate Learning for Multi-Condition Airfoil Shape Optimization

Isaac Robledo, Alberto Vilariño, Arnau Miró +3

Active multi-fidelity surrogate modeling is developed for multi-condition airfoil shape optimization to reduce high-fidelity CFD cost while retaining RANS-consistent aerodynamic me…

cs.LG2025

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…

physics.flu-dyn2025

Unsteady Thermal and Flow Structures of an Impinging Sweeping Jet

Rodrigo Castellanos, Adrián Martín-Perrino, Elena López-Núñez +1

Sweeping jets are increasingly employed in thermal management and flow control applications due to their inherent unsteadiness and ability to cover wide surface areas. This study i…

cs.LG2024★ 16 cited

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…