A data-driven framework for learning hybrid dynamical systems

DOI10.1063/5.0157669zbMath1537.93157MaRDI QIDQ6548679

Shengyuan Xu, Xian-bin Liu, Jin-qiao Duan, Yong Huang, Yang Li

Publication date: 1 June 2024

Published in: Chaos (Search for Journal in Brave)

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) System identification (93B30) Control/observation systems governed by functional relations other than differential equations (such as hybrid and switching systems) (93C30)

Cites Work

• Unnamed Item
• Unnamed Item
• Discovering governing equations from data by sparse identification of nonlinear dynamical systems
• Identification of switched linear systems via sparse optimization
• Identification of piecewise affine systems via mixed-integer programming.
• A clustering technique for the identification of piecewise affine systems
• Extracting governing laws from sample path data of non-Gaussian stochastic dynamical systems
• Data-driven approximation of the Koopman generator: model reduction, system identification, and control
• A data-driven approach for discovering stochastic dynamical systems with non-Gaussian Lévy noise
• Data driven governing equations approximation using deep neural networks
• Deep learning algorithm for data-driven simulation of noisy dynamical system
• Piecewise-smooth dynamical systems. Theory and applications
• A proposal on machine learning via dynamical systems
• Change-point detection in time-series data by relative density-ratio estimation
• Identification of piecewise affine systems based on statistical clustering technique
• Theory of hybrid dynamical systems and its applications to biological and medical systems
• Resonance near border-collision bifurcations in piecewise-smooth, continuous maps
• From time series to complex networks: The visibility graph
• Bifurcations in Nonsmooth Dynamical Systems
• Extended dynamic mode decomposition with dictionary learning: A data-driven adaptive spectral decomposition of the Koopman operator
• An Algorithm Based on Singular Spectrum Analysis for Change-Point Detection
• Optimal Detection of Changepoints With a Linear Computational Cost
• Model selection for hybrid dynamical systems via sparse regression
• Reservoir Computing
• Extracting stochastic dynamical systems with α-stable Lévy noise from data
• Preasymptotic Stability and Homogeneous Approximations of Hybrid Dynamical Systems
• Data-driven discovery of coordinates and governing equations
• A Bayesian approach to identification of hybrid systems
• A bounded-error approach to piecewise affine system identification
• A Sparsification Approach to Set Membership Identification of Switched Affine Systems
• Hybrid dynamical systems
• Variational Inference for Stochastic Differential Equations
• An end-to-end deep learning approach for extracting stochastic dynamical systems with \(\alpha\)-stable Lévy noise
• Using machine learning to anticipate tipping points and extrapolate to post-tipping dynamics of non-stationary dynamical systems

This page was built for publication: A data-driven framework for learning hybrid dynamical systems