Model adaptive phase space reconstruction

DOI10.1063/5.0194330MaRDI QIDQ6592610

K. Hauke Kraemer, Jayesh M. Dhadphale, Maximilian Gelbrecht, Norbert Marwan, Juergen Kurths, R. I. Sujith

Publication date: 26 August 2024

Published in: Chaos (Search for Journal in Brave)

Mathematics Subject Classification ID

Dynamical systems and ergodic theory (37-XX) Ordinary differential equations (34-XX)

Cites Work

• Unnamed Item
• Discovering governing equations from data by sparse identification of nonlinear dynamical systems
• Reservoir computing approaches to recurrent neural network training
• Using information to generate derivative coordinates from noisy time series
• The infinite number of generalized dimensions of fractals and strange attractors
• Measuring the strangeness of strange attractors
• Extracting qualitative dynamics from experimental data
• Nonlinear prediction of chaotic time series
• State space reconstruction in the presence of noise
• An analytic approach to practical state space reconstruction
• Embedology
• Estimating correlation dimension from a chaotic time series: When does plateau onset occur?
• Practical method for determining the minimum embedding dimension of a scalar time series
• Multilayer feedforward networks are universal approximators
• Predicting spatio-temporal time series using dimension reduced local states
• Differentiable manifolds
• Solving Ordinary Differential Equations I
• A unified approach to attractor reconstruction
• Nonlinear time-series analysis revisited
• Deterministic Nonperiodic Flow
• Analysis of Fluid Flows via Spectral Properties of the Koopman Operator
• Learning representations by back-propagating errors
• Finite-Dimensional Dynamics in Taylor-Couette Flow
• Neural ODE to model and prognose thermoacoustic instability
• Selecting embedding delays: an overview of embedding techniques and a new method using persistent homology

This page was built for publication: Model adaptive phase space reconstruction