Robust learning from noisy, incomplete, high-dimensional experimental data via physically constrained symbolic regression

Reinbold et al. propose a physics-informed data-driven approach that successfully discovers a dynamical model using high-dimensional, noisy and incomplete experimental data describing a weakly turbulent fluid flow. This approach is relevant to other non-equilibrium spatially-extended systems.

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Detalles Bibliográficos
Autores principales: Patrick A. K. Reinbold, Logan M. Kageorge, Michael F. Schatz, Roman O. Grigoriev
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
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Acceso en línea:https://doaj.org/article/91ce2a92429f49c5b56333d6015733b7
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Descripción
Sumario:Reinbold et al. propose a physics-informed data-driven approach that successfully discovers a dynamical model using high-dimensional, noisy and incomplete experimental data describing a weakly turbulent fluid flow. This approach is relevant to other non-equilibrium spatially-extended systems.