SOURCE-LINKED INTELLIGENCE

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

arXiv · AI, language, vision and robotics · article · Aug 27, 2026 · UTC

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 neural networks) for the mode approximation, the proposed method leverages a power-iteration scheme that directly learns the dominant Koopman modes without exp

Read original source ↗ Open in workspace

recordType

paper

region

Global