A rational ansatz for the approximation of Koopman eigenfunctions

Abstract Koopman operator theory offers a basis for the systematic transformation and linearization of complex dynamical systems. We propose a method to approximate eigenfunctions of the Koopman operator for sufficiently smooth, deterministic and autonomous dynamical systems with hyperbolic fixed points in an equation‐based context. Approximations of the eigenfunctions are obtained in form of a rational ansatz whose coefficients are determined by minimizing a residual through a bi‐quadratic optimization problem. In addition, we consider an extension of the Hartman‐Grobman theorem, which was first proposed by Lan and Mezić in 2013, as a linear constraint. The implementation for a damped pendulum shows that the approach works in general, however, the optimization problem is non‐convex and thus sensible w. r. t. initial conditions, and increases proportional to the number of ansatz functions to the power of four.

Authors

Institutions

Publication Details

Journal
PAMM
Published
2024-09-30
DOI
https://doi.org/10.1002/pamm.202400187
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A rational ansatz for the approximation of Koopman eigenfunctions

Ulrich J. Römer, Margarete Breitenhuber
PAMM
Model Reduction and Neural Networks
article

A rational ansatz for the approximation of Koopman eigenfunctions

Ulrich J. Römer, Margarete Breitenhuber
article en

Abstract

Abstract Koopman operator theory offers a basis for the systematic transformation and linearization of complex dynamical systems. We propose a method to approximate eigenfunctions of the Koopman operator for sufficiently smooth, deterministic and autonomous dynamical systems with hyperbolic fixed points in an equation‐based context. Approximations of the eigenfunctions are obtained in form of a rational ansatz whose coefficients are determined by minimizing a residual through a bi‐quadratic optimization problem. In addition, we consider an extension of the Hartman‐Grobman theorem, which was first proposed by Lan and Mezić in 2013, as a linear constraint. The implementation for a damped pendulum shows that the approach works in general, however, the optimization problem is non‐convex and thus sensible w. r. t. initial conditions, and increases proportional to the number of ansatz functions to the power of four.

PAMMVol. 24(3)
Karlsruhe Institute of Technology (DE)
Openalex Percentile: Top 84%
Model Reduction and Neural Networks
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.