Echo state networks for modeling turbulent convection
Abstract Turbulent Rayleigh-Bénard convection (RBC) is one of the very prominent examples of chaos in fluid dynamics with significant relevance in nature. Meanwhile, Echo State Networks (ESN) are among the most fundamental machine learning algorithms suited for modeling sequential data. The current...
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Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-79756-7 |
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| author | Mohammad Sharifi Ghazijahani Christian Cierpka |
| author_facet | Mohammad Sharifi Ghazijahani Christian Cierpka |
| author_sort | Mohammad Sharifi Ghazijahani |
| collection | DOAJ |
| description | Abstract Turbulent Rayleigh-Bénard convection (RBC) is one of the very prominent examples of chaos in fluid dynamics with significant relevance in nature. Meanwhile, Echo State Networks (ESN) are among the most fundamental machine learning algorithms suited for modeling sequential data. The current study conducts reduced order modeling of experimental RBC. The ESN successfully models the flow qualitatively. Even for this highly turbulent flow, it is challenging to distinguish predictions from the ground truth. The statistical convergence of the ESN goes beyond the velocity values and is represented in secondary aspects of the flow dynamics, such as spatial and temporal derivatives and vortices. Finally, ESN’s main hyperparameters show values for best performance in strong relation to the flow dynamics. These findings from both the fluid dynamics and computer science perspective set the ground for future informed design of ESNs to tackle one of the most challenging problems in nature: turbulence. |
| format | Article |
| id | doaj-art-741aac79072b47c3bea6e96f3e2e27d0 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-741aac79072b47c3bea6e96f3e2e27d02024-12-08T12:27:13ZengNature PortfolioScientific Reports2045-23222024-12-0114111210.1038/s41598-024-79756-7Echo state networks for modeling turbulent convectionMohammad Sharifi Ghazijahani0Christian Cierpka1Institute of Thermodynamics and Fluid Mechanics, Technische Universität IlmenauInstitute of Thermodynamics and Fluid Mechanics, Technische Universität IlmenauAbstract Turbulent Rayleigh-Bénard convection (RBC) is one of the very prominent examples of chaos in fluid dynamics with significant relevance in nature. Meanwhile, Echo State Networks (ESN) are among the most fundamental machine learning algorithms suited for modeling sequential data. The current study conducts reduced order modeling of experimental RBC. The ESN successfully models the flow qualitatively. Even for this highly turbulent flow, it is challenging to distinguish predictions from the ground truth. The statistical convergence of the ESN goes beyond the velocity values and is represented in secondary aspects of the flow dynamics, such as spatial and temporal derivatives and vortices. Finally, ESN’s main hyperparameters show values for best performance in strong relation to the flow dynamics. These findings from both the fluid dynamics and computer science perspective set the ground for future informed design of ESNs to tackle one of the most challenging problems in nature: turbulence.https://doi.org/10.1038/s41598-024-79756-7Echo state networksTurbulenceRayleigh-Bénard convectionReduced order modeling |
| spellingShingle | Mohammad Sharifi Ghazijahani Christian Cierpka Echo state networks for modeling turbulent convection Scientific Reports Echo state networks Turbulence Rayleigh-Bénard convection Reduced order modeling |
| title | Echo state networks for modeling turbulent convection |
| title_full | Echo state networks for modeling turbulent convection |
| title_fullStr | Echo state networks for modeling turbulent convection |
| title_full_unstemmed | Echo state networks for modeling turbulent convection |
| title_short | Echo state networks for modeling turbulent convection |
| title_sort | echo state networks for modeling turbulent convection |
| topic | Echo state networks Turbulence Rayleigh-Bénard convection Reduced order modeling |
| url | https://doi.org/10.1038/s41598-024-79756-7 |
| work_keys_str_mv | AT mohammadsharifighazijahani echostatenetworksformodelingturbulentconvection AT christiancierpka echostatenetworksformodelingturbulentconvection |