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Noise-induced enhancement of regime lifetimes : A data-driven approach using deterministic trajectories

Titelangaben

Schoeller, Henry ; Chemnitz, Robin ; Koltai, Peter ; Engel, Maximilian ; Pfahl, Stephan:
Noise-induced enhancement of regime lifetimes : A data-driven approach using deterministic trajectories.
arXiv , 2026
DOI: https://doi.org/10.48550/arXiv.2604.27991

Volltext

Link zum Volltext (externe URL): Volltext

Angaben zu Projekten

Projekttitel:
Offizieller Projekttitel
Projekt-ID
SFB 1114: Skalenkaskaden in komplexen Systemen
235221301

Projektfinanzierung: Deutsche Forschungsgemeinschaft

Abstract

We investigate the lifetime of dynamical regimes under the impact of noise motivated by low-dimensional models of the atmosphere. One may expect that the inclusion of noise tends to make the system leave prescribed regions of the state space faster. However, for relevant systems with complexities ranging from phenomenological toy models to reduced models of atmospheric dynamics, this intuition has proven misleading. As long as the noise is sufficiently small, the noisy system stays in regimes of interest on average longer than its deterministic counterpart, an effect we call ``stochastic inertia''. This phenomenon has been observed through extensive numerical simulations for different noise levels. We propose a numerical technique for testing the occurrence of stochastic inertia, constructing, for any fixed noise level, a Markov chain on the set of points given by a sufficiently long trajectory of the system without noise. The method is shown to correctly predict the presence of stochastic inertia in simple systems, and its utility is demonstrated on a paradigm model of atmospheric dynamics.

Weitere Angaben

Publikationsform: Preprint, Postprint
Institutionen der Universität: Fakultäten > Fakultät für Mathematik, Physik und Informatik > Mathematisches Institut > Lehrstuhl Dynamical Systems and Data > Lehrstuhl Dynamical Systems and Data - Univ.-Prof. Dr. Peter Koltai
Titel an der UBT entstanden: Ja
Themengebiete aus DDC: 500 Naturwissenschaften und Mathematik > 510 Mathematik
Eingestellt am: 05 Mai 2026 05:10
Letzte Änderung: 05 Mai 2026 05:10
URI: https://eref.uni-bayreuth.de/id/eprint/96972