eess.SY · 2026-08-29 · No. 99

Systems and Control, 2026-08-29.

1 new papers in eess.SY. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

1 entries
  1. 01

    Data-driven Koopman mode approximation: A neural power iteration algorithm

    Guillaume O. Berger, Raphaël M. Jungers

    eess.SY · cs.LG · math.OC

    This paper proposes a novel data-driven algorithm to approximate the dominant eigenfunctions (aka.~modes) of the Koopman operator of nonlinear dynamical systems using neural networks. The relevance of learning the dominant Koopman modes is to approximate nonlinear dynamics by linear ones in a lifted space, thereby enabling simplified control and analysis. To fight the curse of dimensionality arising from using expressive templates (here...

    arxiv.org/abs/2608.26943 · PDF

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