Reservoir computing model of two-dimensional turbulent convection

2020/01/31 by Sandeep Pandey, Jörg Schumacher
Computer Science · Physics and Astronomy · #Convection #Environmental science #Geology #Mechanics #Meteorology #Model Reduction and Neural Networks #Neural Networks and Applications #Neural Networks and Reservoir Computing #Physics #Statistical physics #Turbulence #cs.CE #cs.LG #physics.flu-dyn

paper · pdf · doi:10.1103/physrevfluids.5.113506

published as Phys. Rev. Fluids 5, 113506 (2020) · 16 pages, 12 figures

arxiv created 2020/10/27 · openalex publication_date 2020/11/19 · arxiv updated 2020/11/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

Reservoir computing models are one possible architecture of recurrent neural networks. Here, a reservoir computing model is applied to reproduce the low-order statistics of a two-dimensional turbulent Rayleigh-B'enard flow without solving the underlying Boussinesq equations.

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