Using machine learning to predict extreme events in the Hénon map

DOI10.1063/1.5121844zbMath1434.68419arXiv2002.10268OpenAlexW3000464944WikidataQ89512509 ScholiaQ89512509MaRDI QIDQ5218150

Jonathan Prexl, Martin Lellep, Bruno Eckhardt, Moritz Linkmann

Publication date: 28 February 2020

Published in: Chaos: An Interdisciplinary Journal of Nonlinear Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2002.10268

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Learning and adaptive systems in artificial intelligence (68T05) Simulation of dynamical systems (37M05) Topological entropy (37B40)

Related Items (4)

Extreme events in dynamical systems and random walkers: a review ⋮ Interpreted machine learning in fluid dynamics: explaining relaminarisation events in wall-bounded shear flows ⋮ A filtered Hénon map ⋮ Machine learning, alignment of covariant Lyapunov vectors, and predictability in Rikitake’s geomagnetic dynamo model

Cites Work

• Unnamed Item
• Unnamed Item
• A two-dimensional mapping with a strange attractor
• Transition to turbulence in shear flows
• Using machine learning to replicate chaotic attractors and calculate Lyapunov exponents from data
• Deterministic Nonperiodic Flow
• Chaos in Dynamical Systems
• Edge states intermediate between laminar and turbulent dynamics in pipe flow

This page was built for publication: Using machine learning to predict extreme events in the Hénon map