Numerical third-order Hopf form for ordinary differential equations

Abstract A data-driven approach has been developed to obtain the numerical third-order Hopf form for two-dimensional ODEs. By using a single numerical solution, the flow on the 2D phase-plane is recovered by a least-squares fit. We then calculate the Poincaré-Lyapunov constant. Sensitivity analysis is carried out to determine the accuracy of the calculated Poincaré-Lyapunov constant; a main result is a formula to compute the relative error of the Poincaré-Lyapunov constant from the errors of the identified coefficients in the third-order Hopf form. We also provide a sufficient condition for correctly identifying the criticality of the system (the sign of the Poincaré-Lyapunov constant). A performance metric is defined for our identification algorithm and this metric is computed by Monte Carlo simulations. Our method achieves good accuracy.

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Publication Details

Journal
Nonlinear Dynamics
Published
2025-06-12
DOI
https://doi.org/10.1007/s11071-025-11351-5
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
0.00

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article

Numerical third-order Hopf form for ordinary differential equations

Tamás Kalmár‐Nagy, Dávid András Horváth
Nonlinear Dynamics
Model Reduction and Neural Networks
article

Numerical third-order Hopf form for ordinary differential equations

Tamás Kalmár‐Nagy, Dávid András Horváth
article en

Abstract

Abstract A data-driven approach has been developed to obtain the numerical third-order Hopf form for two-dimensional ODEs. By using a single numerical solution, the flow on the 2D phase-plane is recovered by a least-squares fit. We then calculate the Poincaré-Lyapunov constant. Sensitivity analysis is carried out to determine the accuracy of the calculated Poincaré-Lyapunov constant; a main result is a formula to compute the relative error of the Poincaré-Lyapunov constant from the errors of the identified coefficients in the third-order Hopf form. We also provide a sufficient condition for correctly identifying the criticality of the system (the sign of the Poincaré-Lyapunov constant). A performance metric is defined for our identification algorithm and this metric is computed by Monte Carlo simulations. Our method achieves good accuracy.

Nonlinear DynamicsVol. 113(17)
Budapest University of Technology and Economics (HU)
Budapesti Műszaki és Gazdaságtudományi Egyetem, Nemzeti Kutatási Fejlesztési és Innovációs Hivatal, Nemzeti Kutatási, Fejlesztési és Innovaciós Alap
Openalex Percentile: Top 89%
Model Reduction and Neural Networks
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