6 papers
A Hybrid Generative Reduced-Order Model for the Minimal Flow Unit
Niccolò Tonioni, Lionel Agostini, Marcial Sanchis-Agudo +4
A data-driven reduced-order modelling framework is proposed for wall-bounded turbulent flows to forecast the intermittent near-wall dynamics over extended time horizons from sparse…
X-CAL: Explaining latent causality in physical space for fluid mechanics
Marcial Sanchis-Agudo, Andrés Cremades, Alvaro Martinez-Sanchez +2
We present X-CAL, a pipeline that combines a -variational autoencoder (-VAE) with the synergistic-unique-redundant decomposition (SURD)~\cite{surd} approach for causality a…
A Mixed-Metric Two-Field Framework for Turbulence: Emergent Stress Anisotropy and Wall Asymptotics from a Single Scalar
Marcial Sanchis-Agudo, Ricardo Vinuesa
In our previous work~\cite{SanchisAgudoVinuesa2025PRL}, we argued that viscous dissipation in turbulence can be understood as the macroscopic imprint of microscopic path uncertaint…
A Geometric Foundation for the Universal Laws of Turbulence
Marcial Sanchis-Agudo, Ricardo Vinuesa
We propose a theoretical framework where the dissipative structures of turbulence emerge from microscopic path uncertainty. By modeling fluid parcels as stochastic tracers governed…
Easy attention: A simple attention mechanism for temporal predictions with transformers
Marcial Sanchis-Agudo, Yuning Wang, Roger Arnau +4
To improve the robustness of transformer neural networks used for temporal-dynamics prediction of chaotic systems, we propose a novel attention mechanism called easy attention whic…
On Deep-Learning-Based Closures for Algebraic Surrogate Models of Turbulent Flows
Benet Eiximeno, Marcial SanchÃs-Agudo, Arnau Miró +3
A deep-learning-based closure model to address energy loss in low-dimensional surrogate models based on proper-orthogonal-decomposition (POD) modes is introduced. Using a transform…