Author

S. Le Clainche

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Oct 2026

A physics-based deep learning framework for super-resolution in fluid dynamics based on higher-order singular value decomposition

In this work, a novel methodology combining higher-order singular value decomposition (HOSVD) with deep learning is presented for the super-resolution (SR) of computational fluid dynamics and sparse experimental datasets. This approach, referred to as HOSVD-SR (The software is available at https://modelflows.github.io/...

G. Barragan, A. Hetherington, R. Abad'ia-Heredia et al. · The Physics of Fluids · 0 citations

#machine learning May 2026

AeroJEPA: Learning Semantic Latent Representations for Scalable 3D Aerodynamic Field Modeling

In this selective-decoding experiment, AeroJEPA substantially outperforms the evaluated direct-regression surrogates while avoiding predictions over the remainder of the aircraft, and shows how predictive representations can support aerodynamic analysis with or without full-field reconstruction.

Francisco Giral, A. Vishwasrao, Andrea Arroyo Ramo et al. · arXiv.org · 1 citation

Preprint Aug 2026

A hybrid proper orthogonal decomposition and diffusion framework for reduced-order forecasting of turbulent flow dynamics

Forecasting turbulent flow dynamics requires a balance between predictive fidelity and computational efficiency. Diffusion-based generative models can represent complex spatiotemporal dynamics, but their application to high-dimensional turbulent flows remains computationally expensive. In contrast, proper orthogonal de...

Rodrigo Abad'ia-Heredia, Xiangrui Zou, M. López-Martín et al. · 0 citations