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Good and bad predictions: Assessing and improving the replication of chaotic attractors by means of reservoir computing

Haluszczynski, Alexander und Räth, Christoph (2019) Good and bad predictions: Assessing and improving the replication of chaotic attractors by means of reservoir computing. Chaos, 29, Seite 103143. American Institute of Physics (AIP). doi: 10.1063/1.5118725. ISSN 1054-1500.

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Offizielle URL: https://aip.scitation.org/doi/10.1063/1.5118725

Kurzfassung

The prediction of complex nonlinear dynamical systems with the help of machine learning techniques has become increasingly popular. In particular, reservoir computing turned out to be a very promising approach especially for the reproduction of the long-term properties of a nonlinear system. Yet, a thorough statistical analysis of the forecast results is missing. Using the Lorenz and Rössler system, we statistically analyze the quality of prediction for different parametrizations - both the exact short-term prediction as well as the reproduction of the longterm properties (the climate) of the system as estimated by the correlation dimension and largest Lyapunov exponent. We find that both short- and long-term predictions vary significantly among the realizations. Thus, special care must be taken in selecting the good predictions as realizations, which deliver better short-term prediction also tend to better resemble the long-term climate of the system. Instead of only using purely random Erdös-Renyi networks, we also investigate the benefit of alternative network topologies such as small world or scalefree networks and show which effect they have on the prediction quality. Our results suggest that the overall performance with respect to the reproduction of the climate of both the Lorenz and Rössler system is worst for scale-free networks. For the Lorenz system, there seems to be a slight benefit of using small world networks, while for the Rössler system, small world and Erdös-Renyi networks performed equivalently well. In general, the observation is that reservoir computing works for all network topologies investigated here.

elib-URL des Eintrags:https://elib.dlr.de/129947/
Dokumentart:Zeitschriftenbeitrag
Titel:Good and bad predictions: Assessing and improving the replication of chaotic attractors by means of reservoir computing
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Haluszczynski, AlexanderLMUNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Räth, ChristophChristoph.Raeth (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Datum:25 Oktober 2019
Erschienen in:Chaos
Referierte Publikation:Ja
Open Access:Ja
Gold Open Access:Nein
In SCOPUS:Ja
In ISI Web of Science:Ja
Band:29
DOI:10.1063/1.5118725
Seitenbereich:Seite 103143
Verlag:American Institute of Physics (AIP)
ISSN:1054-1500
Status:veröffentlicht
Stichwörter:Machine Learning, Complex Systems, Prediction, Reservoir Computing, Networks, Attractors
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Raumfahrt
HGF - Programmthema:Forschung unter Weltraumbedingungen
DLR - Schwerpunkt:Raumfahrt
DLR - Forschungsgebiet:R FR - Forschung unter Weltraumbedingungen
DLR - Teilgebiet (Projekt, Vorhaben):R - Komplexe Plasmen / Datenanalyse (alt)
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Institut für Materialphysik im Weltraum > Gruppe Komplexe Plasmen
Hinterlegt von: Räth, Christoph
Hinterlegt am:04 Nov 2019 09:19
Letzte Änderung:04 Nov 2019 09:19

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