1 citations · 2 across the 5 of their papers we have counts for
5 papers
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…
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 ana…
Diff-SPORT: Diffusion-based Sensor Placement Optimization and Reconstruction of Turbulent flows in urban environments
Abhijeet Vishwasrao, Sai Bharath Chandra Gutha, Andres Cremades +6
Rapid urbanization demands accurate and efficient monitoring of turbulent wind patterns to support air quality, climate resilience and infrastructure design. Traditional sparse rec…
Assessment of non-intrusive sensing in wall-bounded turbulence through explainable deep learning
A. Cremades, R. Freibergs, S. Hoyas +3
In this work we present a framework to explain the prediction of the velocity fluctuation at a certain wall-normal distance from wall measurements with a deep-learning model. For t…
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…