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
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/91ce2a92429f49c5b56333d6015733b7
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spelling	oai:doaj.org-article:91ce2a92429f49c5b56333d6015733b72021-12-02T15:00:50ZRobust learning from noisy, incomplete, high-dimensional experimental data via physically constrained symbolic regression10.1038/s41467-021-23479-02041-1723https://doaj.org/article/91ce2a92429f49c5b56333d6015733b72021-05-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-23479-0https://doaj.org/toc/2041-1723Reinbold 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.Patrick A. K. ReinboldLogan M. KageorgeMichael F. SchatzRoman O. GrigorievNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Patrick A. K. Reinbold Logan M. Kageorge Michael F. Schatz Roman O. Grigoriev Robust learning from noisy, incomplete, high-dimensional experimental data via physically constrained symbolic regression
description	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.
format	article
author	Patrick A. K. Reinbold Logan M. Kageorge Michael F. Schatz Roman O. Grigoriev
author_facet	Patrick A. K. Reinbold Logan M. Kageorge Michael F. Schatz Roman O. Grigoriev
author_sort	Patrick A. K. Reinbold
title	Robust learning from noisy, incomplete, high-dimensional experimental data via physically constrained symbolic regression
title_short	Robust learning from noisy, incomplete, high-dimensional experimental data via physically constrained symbolic regression
title_full	Robust learning from noisy, incomplete, high-dimensional experimental data via physically constrained symbolic regression
title_fullStr	Robust learning from noisy, incomplete, high-dimensional experimental data via physically constrained symbolic regression
title_full_unstemmed	Robust learning from noisy, incomplete, high-dimensional experimental data via physically constrained symbolic regression
title_sort	robust learning from noisy, incomplete, high-dimensional experimental data via physically constrained symbolic regression
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/91ce2a92429f49c5b56333d6015733b7
work_keys_str_mv	AT patrickakreinbold robustlearningfromnoisyincompletehighdimensionalexperimentaldataviaphysicallyconstrainedsymbolicregression AT loganmkageorge robustlearningfromnoisyincompletehighdimensionalexperimentaldataviaphysicallyconstrainedsymbolicregression AT michaelfschatz robustlearningfromnoisyincompletehighdimensionalexperimentaldataviaphysicallyconstrainedsymbolicregression AT romanogrigoriev robustlearningfromnoisyincompletehighdimensionalexperimentaldataviaphysicallyconstrainedsymbolicregression
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Robust learning from noisy, incomplete, high-dimensional experimental data via physically constrained symbolic regression

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