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Learning Lyapunov Operators for Nonlinear Systems

Paper recorded by Signals 4 on 2026-09-16 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-16 on arXiv · recorded by Signals 4 on 2026-09-17

Category: cs.LG · 机器学习 · first seen 2026-09-17

Abstract

Constructing Lyapunov functions for nonlinear dynamical systems is a central problem in stability analysis, yet remains challenging. Lyapunov functions are commonly characterized as solutions to first-order partial differential equations (PDEs), but these solutions are typically obtained for single systems, limiting their reuse across systems. In this paper, we study the Lyapunov solution operator

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#22 most recent of 215 cs.LG papers we have recorded · ↑ newer: Fast Learning Rates for Physics-Informed Kernel Methods · ↓ older: Preventing Model Collapse: A Fisher-Rao Perspective on the Dynamics of
Cite this page: Learning Lyapunov Operators for Nonlinear Systems: the #22 most recent of 215 cs.LG papers we have recorded (as of 2026-09-16). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/learning-lyapunov-operators-for-nonlinear-systems.html
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