Data-driven Koopman mode approximation: A neural power iteration algorithm
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 explicitly constructing the projection of the Koopman operator on the template of functions. Our app...
| Authors | Guillaume O. Berger, Raphaël M. Jungers |
|---|---|
| Primary category | eess.SY |
| Published | 27 August 2026 |
| Last updated at source | 20 September 2026 |
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