Journal article
Identifying regions of importance in wall-bounded turbulence through explainable deep learning
A Cremades, S Hoyas, R Deshpande, P Quintero, M Lellep, WJ Lee, JP Monty, N Hutchins, M Linkmann, I Marusic, R Vinuesa
Nature Communications | NATURE PORTFOLIO | Published : 2024
Abstract
Despite its great scientific and technological importance, wall-bounded turbulence is an unresolved problem in classical physics that requires new perspectives to be tackled. One of the key strategies has been to study interactions among the energy-containing coherent structures in the flow. Such interactions are explored in this study using an explainable deep-learning method. The instantaneous velocity field obtained from a turbulent channel flow simulation is used to predict the velocity field in time through a U-net architecture. Based on the predicted flow, we assess the importance of each structure for this prediction using the game-theoretic algorithm of SHapley Additive exPlanations ..
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Grants
Awarded by Australian Research Council
Funding Acknowledgements
The deep-learning-model training was enabled by resources provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS) at Berzelius (NSC), partially funded by the Swedish Research Council through grant agreement no. 2022-06725. This project has been partially funded by the Spanish Ministry of Science, Innovation, and University through the University Faculty Training (FPU) program with reference FPU19/02201 (AC). The data has been obtained with support of grant PID2021-128676OB-I00 funded by MCIN/AEI/10.13039/ 501100011033 and by "ERDF A way of making Europe", by the European Union (SH). RV acknowledges the financial support from ERC grant no. 2021-CoG-101043998, DEEPCONTROL. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them. RD acknowledges the financial support from the Melbourne Postdoctoral Fellowship of the University of Melbourne. R.D., J.L., J.P.M., N.H., and I.M. are grateful to the ARC (Australian Research Council) for their continuous financial support.