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Sequentially learning regions of attraction from data
Oumayma Khattabi, Matteo Tacchi, Sorin Olaru

Sequentially learning regions of attraction from data

Science & Mathematics Published 2025 Verification Pending

Overview & Synopsis

The paper is dedicated to data-driven analysis of dynamical systems. It deals with certifying the basin of attraction of a stable equilibrium for an unknown dynamical system. It is supposed that point-wise evaluation of the right-hand side of the ordinary differential equation governing the system is available for a set of points in the state space. Technically, a Piecewise Affine Lyapunov function will be constructed iteratively using an optimisation-based technique for the effective validation of the certificates. As a main contribution, whenever those certificates are violated locally, a refinement of the domain and the associated tessellation is produced, thus leading to an improvement in the description of the domain of attraction.

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Document Information

Document Title Sequentially learning regions of attraction from data
Author / Organization Oumayma Khattabi, Matteo Tacchi, Sorin Olaru
Subject & Discipline Multidisciplinary Academic Open Research
Categories Science & Mathematics
Publication Year 2025
License / Rights Open Access / Creative Commons
Resource Verification Listed PDF (Verification Pending)
Original Host Website HAL Open Science (hal.science)
File Format application/pdf (Portable Document Format)
Language EN
Catalog ID DOC-217498
Original Source Link Visit Source Page on hal.science »