Data-driven predictions of the Lorenz system
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Publication:2115546
DOI10.1016/j.physd.2020.132495OpenAlexW3016221005MaRDI QIDQ2115546
Laurent Perret, Pierre Dubois, Thomas Gomez, Laurent Planckaert
Publication date: 17 March 2022
Published in: Physica D (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.physd.2020.132495
Learning and adaptive systems in artificial intelligence (68T05) Strange attractors, chaotic dynamics of systems with hyperbolic behavior (37D45) Time series analysis of dynamical systems (37M10) Complex behavior and chaotic systems of ordinary differential equations (34C28) Numerical chaos (65P20)
Related Items (3)
Machine learning for fluid flow reconstruction from limited measurements ⋮ A nonintrusive hybrid neural-physics modeling of incomplete dynamical systems: Lorenz equations ⋮ Kernel-based parameter estimation of dynamical systems with unknown observation functions
Uses Software
Cites Work
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