Understanding and modeling the dynamics of multiscale systems is a problem of considerable interest both for theory and applications. For unavoidable practical reasons, in multiscale systems, there is the need to eliminate from the description the fast and small-scale degrees of freedom and thus build effective models for only the slow and large-scale degrees of freedom. When there is a wide scale separation between the degrees of freedom, asymptotic techniques, such as the adiabatic approximation, can be used for devising such effective models, while away from this limit there exist no systematic techniques. Here, we scrutinize the use of machine learning, based on reservoir computing, to build data-driven effective models of multiscale chaotic systems. We show that, for a wide scale separation, machine learning generates effective models akin to those obtained using multiscale asymptotic techniques and, remarkably, remains effective in predictability also when the scale separation is reduced. We also show that predictability can be improved by hybridizing the reservoir with an imperfect model.

Effective models and predictability of chaotic multiscale systems via machine learning / Borra, F.; Vulpiani, A.; Cencini, M.. - In: PHYSICAL REVIEW. E. - ISSN 2470-0045. - 102:5(2020), p. 052203. [10.1103/PhysRevE.102.052203]

Effective models and predictability of chaotic multiscale systems via machine learning

Borra F.;Vulpiani A.;Cencini M.
2020

Abstract

Understanding and modeling the dynamics of multiscale systems is a problem of considerable interest both for theory and applications. For unavoidable practical reasons, in multiscale systems, there is the need to eliminate from the description the fast and small-scale degrees of freedom and thus build effective models for only the slow and large-scale degrees of freedom. When there is a wide scale separation between the degrees of freedom, asymptotic techniques, such as the adiabatic approximation, can be used for devising such effective models, while away from this limit there exist no systematic techniques. Here, we scrutinize the use of machine learning, based on reservoir computing, to build data-driven effective models of multiscale chaotic systems. We show that, for a wide scale separation, machine learning generates effective models akin to those obtained using multiscale asymptotic techniques and, remarkably, remains effective in predictability also when the scale separation is reduced. We also show that predictability can be improved by hybridizing the reservoir with an imperfect model.
2020
Chaotic dynamical systems, machine learning, multiscale systems
01 Pubblicazione su rivista::01a Articolo in rivista
Effective models and predictability of chaotic multiscale systems via machine learning / Borra, F.; Vulpiani, A.; Cencini, M.. - In: PHYSICAL REVIEW. E. - ISSN 2470-0045. - 102:5(2020), p. 052203. [10.1103/PhysRevE.102.052203]
File allegati a questo prodotto
File Dimensione Formato  
Borra_Effective models_2020.pdf

accesso aperto

Tipologia: Documento in Pre-print (manoscritto inviato all'editore, precedente alla peer review)
Licenza: Creative commons
Dimensione 2.1 MB
Formato Adobe PDF
2.1 MB Adobe PDF
Borra_Effective models and predictability_2020.pdf

solo gestori archivio

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 1.31 MB
Formato Adobe PDF
1.31 MB Adobe PDF   Contatta l'autore

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1469841
Citazioni
  • ???jsp.display-item.citation.pmc??? 1
  • Scopus 12
  • ???jsp.display-item.citation.isi??? 10
social impact