Identifying regions of importance in wall-bounded turbulence through explainable deep learning

Autor: Andrés Cremades, Sergio Hoyas, Rahul Deshpande, Pedro Quintero, Martin Lellep, Will Junghoon Lee, Jason P. Monty, Nicholas Hutchins, Moritz Linkmann, Ivan Marusic, Ricardo Vinuesa

Publikováno v: Nature Communications, Vol 15, Iss 1, Pp 1-12 (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

Externí odkaz: https://doaj.org/article/bad246b40a5c4e169d43de69a1f29ec7

Effects of anisotropy on the geometry of tracer particle trajectories in turbulent flows

Autor: Yasmin Hengster, Martin Lellep, Julian Weigel, Matthew Bross, Johannes Bosbach, Daniel Schanz, Andreas Schröder, Florian Huhn, Matteo Novara, Daniel Garaboa-Paz, Christian Kähler, Moritz Linkmann

Using curvature and torsion to describe Lagrangian trajectories gives a full description of these as well as an insight into small and large time scales as temporal derivatives up to order 3 are involved. One might expect that the statistics of these

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1bdc8e15d6314fee99516e1b961c8feb

Interpreted machine learning in fluid dynamics: explaining relaminarisation events in wall-bounded shear flows

Autor: Martin Lellep, Jonathan Prexl, Bruno Eckhardt, Moritz Linkmann

Publikováno v: Journal of Fluid Mechanics. 942

Machine Learning (ML) is becoming increasingly popular in fluid dynamics. Powerful ML algorithms such as neural networks or ensemble methods are notoriously difficult to interpret. Here, we introduce the novel Shapley Additive Explanations (SHAP) alg

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::59a27e27129225685780da3c4e4e4c5f
https://doi.org/10.1017/jfm.2022.307

Using machine learning to predict extreme events in the Hénon map

Autor: Martin Lellep, Jonathan Prexl, Moritz Linkmann, Bruno Eckhardt

Publikováno v: Lellep, M, Prexl, J, Linkmann, M & Eckhardt, B 2020, ' Using Machine Learning to predict extreme events in the Hénon map ', Chaos, vol. 30, no. 1, 013113 . https://doi.org/10.1063/1.5121844

Machine Learning (ML) inspired algorithms provide a flexible set of tools for analyzing and forecasting chaotic dynamical systems. We here analyze the performance of one algorithm for the prediction of extreme events in the two-dimensional H\'enon ma

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5e6aa14401ea1ee1fdd57c769fb2221b
https://pubmed.ncbi.nlm.nih.gov/32013508

Linear stability analysis of purely elastic travelling-wave solutions in pressure-driven channel flows.

Autor: Lellep, Martin, Linkmann, Moritz, Morozov, Alexander

Publikováno v: Journal of Fluid Mechanics; 3/22/2023, Vol. 959, p1-10, 10p