Model-free inference of unseen attractors: reconstructing phase space features from a single noisy trajectory using Reservoir computing

DOI10.1063/5.0065813zbMATH Open1548.3713MaRDI QIDQ6557728

Could not fetch data.

Publication date: 18 June 2024

Published in: Chaos (Search for Journal in Brave)

Mathematics Subject Classification ID

Time series analysis of dynamical systems (37M10) Simulation of dynamical systems (37M05) Computational methods for invariant manifolds of dynamical systems (37M21)

Cites Work

• Discovering governing equations from data by sparse identification of nonlinear dynamical systems
• Data driven governing equations approximation using deep neural networks
• Nonlinear system identification. NARMAX methods in the time, frequency, and spatio-temporal domains
• Coexisting Hidden Attractors in a 4-D Simplified Lorenz System
• Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations
• Using machine learning to replicate chaotic attractors and calculate Lyapunov exponents from data
• Good and bad predictions: Assessing and improving the replication of chaotic attractors by means of reservoir computing
• Reducing network size and improving prediction stability of reservoir computing
• Detecting unstable periodic orbits based only on time series: When adaptive delayed feedback control meets reservoir computing

Related Items (4)

Effect of temporal resolution on the reproduction of chaotic dynamics via reservoir computing ⋮ A tighter generalization bound for reservoir computing ⋮ Model-free prediction of multistability using echo state network ⋮ Learning unseen coexisting attractors

This page was built for publication: Model-free inference of unseen attractors: reconstructing phase space features from a single noisy trajectory using Reservoir computing

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6557728)