Data-driven model identification using forcing-induced limit cycles
From MaRDI portal
Publication:6191513
DOI10.1016/j.physd.2023.134013OpenAlexW4389305897MaRDI QIDQ6191513
No author found.
Publication date: 7 March 2024
Published in: Physica D (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.physd.2023.134013
model reductionneurosciencephase modelKoopman analysisphase-amplitude reductiondata-driven model identification
Applications of dynamical systems (37Nxx) Approximation methods and numerical treatment of dynamical systems (37Mxx) Controllability, observability, and system structure (93Bxx)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Discovering governing equations from data by sparse identification of nonlinear dynamical systems
- Chemical oscillations, waves, and turbulence
- A data-driven approximation of the koopman operator: extending dynamic mode decomposition
- Mathematical foundations of neuroscience
- Isochrons and phaseless sets
- Machine learning of linear differential equations using Gaussian processes
- Phase reduction and phase-based optimal control for biological systems: a tutorial
- Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control
- Scientific machine learning through physics-informed neural networks: where we are and what's next
- Understanding the dynamics of biological and neural oscillator networks through exact mean-field reductions: a review
- Isostables, isochrons, and Koopman spectrum for the action-angle representation of stable fixed point dynamics
- On dynamic mode decomposition: theory and applications
- A direct method approach for data-driven inference of high accuracy adaptive phase-isostable reduced order models
- Dynamic Mode Decomposition with Control
- Applied Koopmanism
- Dynamic mode decomposition of numerical and experimental data
- Numerical Gaussian Processes for Time-Dependent and Nonlinear Partial Differential Equations
- Controlling chaos
- Ergodic Theory, Dynamic Mode Decomposition, and Computation of Spectral Properties of the Koopman Operator
- Data-Driven Science and Engineering
- Learning partial differential equations via data discovery and sparse optimization
- On the Phase Reduction and Response Dynamics of Neural Oscillator Populations
- Analysis of Fluid Flows via Spectral Properties of the Koopman Operator
- Model selection for hybrid dynamical systems via sparse regression
- Data-driven inference of high-accuracy isostable-based dynamical models in response to external inputs
- An Adaptive Phase-Amplitude Reduction Framework without $\mathcal{O}(\epsilon)$ Constraints on Inputs
- Optimal Control of Oscillation Timing and Entrainment Using Large Magnitude Inputs: An Adaptive Phase-Amplitude-Coordinate-Based Approach
- A data-driven phase and isostable reduced modeling framework for oscillatory dynamical systems
- The geometry of biological time.
This page was built for publication: Data-driven model identification using forcing-induced limit cycles