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pro vyhledávání: '"Jonathan Prexl"'
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
https://doi.org/10.1017/jfm.2022.307
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
https://pubmed.ncbi.nlm.nih.gov/32013508