Combining machine learning with knowledge-based modeling for scalable forecasting and subgrid-scale closure of large, complex, spatiotemporal systems.

Wikner, Alexander; Pathak, Jaideep; Hunt, Brian; Girvan, Michelle; Arcomano, Troy; Szunyogh, Istvan; Pomerance, Andrew; Ott, Edward

Wikner, Alexander; Pathak, Jaideep; Hunt, Brian; Girvan, Michelle; Arcomano, Troy; Szunyogh, Istvan; Pomerance, Andrew; Ott, Edward.

Afiliación

Wikner A; Department of Physics and Institute for Research in Electronics and Applied Physics, University of Maryland, College Park, Maryland 20740, USA.
Pathak J; Department of Physics and Institute for Research in Electronics and Applied Physics, University of Maryland, College Park, Maryland 20740, USA.
Hunt B; Institute for Physical Science and Technology, University of Maryland, College Park, Maryland 20740, USA.
Girvan M; Department of Physics and Institute for Research in Electronics and Applied Physics, University of Maryland, College Park, Maryland 20740, USA.
Arcomano T; Department of Atmospheric Sciences, Texas A&M University, College Station, Texas 77843, USA.
Szunyogh I; Department of Atmospheric Sciences, Texas A&M University, College Station, Texas 77843, USA.
Pomerance A; Potomac Research LLC, Alexandria, Virginia 22311, USA.
Ott E; Department of Physics and Institute for Research in Electronics and Applied Physics, University of Maryland, College Park, Maryland 20740, USA.

Chaos ; 30(5): 053111, 2020 May.

Article en En | MEDLINE | ID: mdl-32491877

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Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Chaos Asunto de la revista: CIENCIA Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos

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