Periodically activated physics-informed neural networks for assimilation tasks for three-dimensional Rayleigh–Bénard convection

Mommert, Michael; Barta, Robin; Bauer, Christian; Volk, Marie-Christine; Wagner, Claus

doi:10.1016/j.compfluid.2024.106419

Computers & Fluids

2024

DOI: 10.1016/j.compfluid.2024.106419

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Periodically activated physics-informed neural networks for assimilation tasks for three-dimensional Rayleigh–Bénard convection

Michael Mommert,

Robin Barta,

Christian Bauer

et al.

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Echo state networks for modeling turbulent convection

Sharifi Ghazijahani,

Cierpka

2024

Sci Rep

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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.

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Echo state networks for modeling turbulent convection

Sharifi Ghazijahani,

Cierpka

2024

Sci Rep

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show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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Periodically activated physics-informed neural networks for assimilation tasks for three-dimensional Rayleigh–Bénard convection

Cited by 1 publication

References 28 publications

Echo state networks for modeling turbulent convection

Echo state networks for modeling turbulent convection

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