3 citations · 9 across the 12 of their papers we have counts for
12 papers
Unveiling wing turbulence dynamics through explainable deep learning
Samuel Molina-Casino, Andrés Cremades, Sergio Hoyas +3
The three-dimensional organization of coherent structures in the turbulent flow over aircraft wings remains elusive, limiting our ability to reduce fuel consumption. Here, we use e…
The near-wall cycle for skin-friction generation revealed through explainable deep learning
Andres Cremades, Sergio Hoyas, Ricardo Vinuesa
Skin friction in wall-bounded turbulence is produced by intermittent near-wall motions, yet conventional coherent-structure definitions do not identify which individual events gene…
Explainable deep learning reveals the physical mechanisms behind the turbulent kinetic energy equation
Francisco Alcántara-Ávila, Andrés Cremades, Sergio Hoyas +1
In this work, we investigate the physical mechanisms governing turbulent kinetic energy transport using explainable deep learning (XDL). An XDL model based on SHapley Additive exPl…
Exploring the interplay between Planetary Boundaries and Sustainable Development Goals using Large Language Models
Lamyae Rhomrasi, Pilar Manchón, Ricardo Vinuesa +4
By analyzing 40,037 climate articles using Large Language Models (LLMs), we identified interactions between Planetary Boundaries (PBs) and Sustainable Development Goals (SDGs). An…
Evaluating Visual Mathematics in Multimodal LLMs: A Multilingual Benchmark Based on the Kangaroo Tests
Arnau Igualde Sáez, Lamyae Rhomrasi, Yusef Ahsini +5
Multimodal Large Language Models (MLLMs) promise advanced vision language capabilities, yet their effectiveness in visually presented mathematics remains underexplored. This paper…
Large language models in climate and sustainability policy: limits and opportunities
Francesca Larosa, Sergio Hoyas, H. Alberto Conejero +3
As multiple crises threaten the sustainability of our societies and pose at risk the planetary boundaries, complex challenges require timely, updated, and usable information. Natur…
Classically studied coherent structures only paint a partial picture of wall-bounded turbulence
Andrés Cremades, Sergio Hoyas, Ricardo Vinuesa
For the last 140 years, the mechanisms of transport and dissipation of energy in a turbulent flow have not been completely understood. Previous research has focused on analyzing th…
Additive-feature-attribution methods: a review on explainable artificial intelligence for fluid dynamics and heat transfer
Andrés Cremades, Sergio Hoyas, Ricardo Vinuesa
The use of data-driven methods in fluid mechanics has surged dramatically in recent years due to their capacity to adapt to the complex and multi-scale nature of turbulent flows, a…
Higher DNS-resolution requirements for expanded overlap region and confirmation of a convergence criterion
Sergio Hoyas, Ricardo Vinuesa, Peter Schmid +1
Direct numerical simulations (DNS) stand out as formidable tools in studying turbulent flows. Despite the fact that the achievable Reynolds number remains lower than those availabl…
A deep-learning approach for reconstructing 3D turbulent flows from 2D observation data
Mustafa Z. Yousif, Linqi Yu, Sergio Hoyas +2
Turbulence is a complex phenomenon that has a chaotic nature with multiple spatio-temporal scales, making predictions of turbulent flows a challenging topic. Nowadays, an abundance…
Aim in Climate Change and City Pollution
Pablo Torres, Beril Sirmacek, Sergio Hoyas +1
The sustainability of urban environments is an increasingly relevant problem. Air pollution plays a key role in the degradation of the environment as well as the health of the citi…
Towards extraction of orthogonal and parsimonious non-linear modes from turbulent flows
Hamidreza Eivazi, Soledad Le Clainche, Sergio Hoyas +1
We propose a deep probabilistic-neural-network architecture for learning a minimal and near-orthogonal set of non-linear modes from high-fidelity turbulent-flow-field data useful f…