dc.contributor.author
Gelbrecht, Maximilian
dc.contributor.author
Lucarini, Valerio
dc.contributor.author
Boers, Niklas
dc.contributor.author
Kurths, Jürgen
dc.date.accessioned
2021-12-01T13:59:51Z
dc.date.available
2021-12-01T13:59:51Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/31692
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-31423
dc.description.abstract
We propose a comprehensive framework able to address both the predictability of the first and of the second kind for high-dimensional chaotic models. For this purpose, we analyse the properties of a newly introduced multistable climate toy model constructed by coupling the Lorenz ’96 model with a zero-dimensional energy balance model. First, the attractors of the system are identified with Monte Carlo Basin Bifurcation Analysis. Additionally, we are able to detect the Melancholia state separating the two attractors. Then, Neural Ordinary Differential Equations are applied to predict the future state of the system in both of the identified attractors.
en
dc.format.extent
11 Seiten
dc.rights.uri
https://creativecommons.org/licenses/by/4.0/
dc.subject
bistable climate toy model
en
dc.subject
physics-based machine learning methods
en
dc.subject.ddc
500 Naturwissenschaften und Mathematik::530 Physik::530 Physik
dc.title
Analysis of a bistable climate toy model with physics-based machine learning methods
dc.type
Wissenschaftlicher Artikel
dcterms.bibliographicCitation.doi
10.1140/epjs/s11734-021-00175-0
dcterms.bibliographicCitation.journaltitle
The European Physical Journal Special Topics
dcterms.bibliographicCitation.number
14-15
dcterms.bibliographicCitation.pagestart
3121
dcterms.bibliographicCitation.pageend
3131
dcterms.bibliographicCitation.volume
230
dcterms.bibliographicCitation.url
https://doi.org/10.1140/epjs/s11734-021-00175-0
refubium.affiliation
Mathematik und Informatik
refubium.resourceType.isindependentpub
no
dcterms.accessRights.openaire
open access
dcterms.isPartOf.eissn
1951-6401
refubium.resourceType.provider
WoS-Alert