Reconstruction, forecasting, and stability of chaotic dynamics from partial data
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Publication:6552156
DOI10.1063/5.0159479zbMATH Open1545.37072MaRDI QIDQ6552156
Luca Magri, Elise Özalp, Georgios Margazoglou
Publication date: 8 June 2024
Published in: Chaos (Search for Journal in Brave)
Strange attractors, chaotic dynamics of systems with hyperbolic behavior (37D45) Computational methods for attractors of dynamical systems (37M22)
Cites Work
- Title not available (Why is that?)
- Title not available (Why is that?)
- Title not available (Why is that?)
- The Lyapunov dimension of strange attractors
- Determining Lyapunov exponents from a time series
- Some global dynamical properties of the Kuramoto-Sivashinsky equations: nonlinear stability and attractors
- The Kuramoto-Sivashinsky equation: a bridge between PDE's and dynamical systems
- Ergodic theory of differentiable dynamical systems
- Fundamental limitations for estimating dimensions and Lyapunov exponents in dynamical systems
- Multilayer feedforward networks are universal approximators
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- A practical method for calculating largest Lyapunov exponents from small data sets
- Covariant Lyapunov vectors
- 10.1162/153244303768966139
- Nonlinear analysis of hydrodynamic instability in laminar flames—I. Derivation of basic equations
- Calculation of the Wasserstein Distance Between Probability Distributions on the Line
- Ergodic theory of chaos and strange attractors
- Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks
- Using machine learning to replicate chaotic attractors and calculate Lyapunov exponents from data
- Deterministic Nonperiodic Flow
- Stability, sensitivity and optimisation of chaotic acoustic oscillations
- Learning latent dynamics for partially observed chaotic systems
- Fourth-Order Time-Stepping for Stiff PDEs
- Predictability: a way to characterize complexity
- Robust optimization and validation of echo state networks for learning chaotic dynamics
- Stability analysis of chaotic systems from data
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