Hybrid differential equations: Integrating mechanistic and data-driven techniques for modelling of water systems.

Quaghebeur, Ward; Torfs, Elena; De Baets, Bernard; Nopens, Ingmar

Quaghebeur, Ward; Torfs, Elena; De Baets, Bernard; Nopens, Ingmar.

Afiliación

Quaghebeur W; BIOMATH, Department of Data Analysis and Mathematical Modelling, Ghent University, 9000 Gent, Belgium; KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, 9000 Gent, Belgium; CAPTURE, Centre for Advanced Process Technology for Urban Resource Recovery, 9000 Gent, Belgium
Torfs E; BIOMATH, Department of Data Analysis and Mathematical Modelling, Ghent University, 9000 Gent, Belgium; CAPTURE, Centre for Advanced Process Technology for Urban Resource Recovery, 9000 Gent, Belgium.
De Baets B; KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, 9000 Gent, Belgium.
Nopens I; BIOMATH, Department of Data Analysis and Mathematical Modelling, Ghent University, 9000 Gent, Belgium; CAPTURE, Centre for Advanced Process Technology for Urban Resource Recovery, 9000 Gent, Belgium.

Water Res ; 213: 118166, 2022 Apr 15.

Article en En | MEDLINE | ID: mdl-35158263

Palabras clave

Data-driven models; Hybrid models; Machine learning; Mechanistic models; Neural differential equations; Water systems

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Texto completo: 1 Bases de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Water Res Año: 2022 Tipo del documento: Article País de afiliación: Bélgica

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