Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.21825 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911618639593472 |
|---|---|
| author | Monfared, Zahra Malhotra, Saksham Hajime, Sekiya Kevrekidis, Ioannis Dietrich, Felix |
| author_facet | Monfared, Zahra Malhotra, Saksham Hajime, Sekiya Kevrekidis, Ioannis Dietrich, Felix |
| contents | For continuous-time dynamical systems with reversible trajectories, the nowhere-vanishing eigenfunctions of the Koopman operator of the system form a multiplicative group. Here, we exploit this property to accelerate the systematic numerical computation of the eigenspaces of the operator. Given a small set of (so-called ``principal'') eigenfunctions that are approximated conventionally, we can obtain a much larger set by constructing polynomials of the principal eigenfunctions. This enriches the set, and thus allows us to more accurately represent application-specific observables. Often, eigenfunctions exhibit localized singularities (e.g. in simple, one-dimensional problems with multiple steady states) or extended ones (e.g. in simple, two-dimensional problems possessing a limit cycle, or a separatrix); we discuss eigenfunction matching/continuation across such singularities. By handling eigenfunction singularities and enabling their continuation, our approach supports learning consistent global representations from locally sampled data. This is particularly relevant for multistable systems and applications with sparse or fragmented measurements. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_21825 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | On the algebra of Koopman eigenfunctions and on some of their infinities Monfared, Zahra Malhotra, Saksham Hajime, Sekiya Kevrekidis, Ioannis Dietrich, Felix Dynamical Systems Machine Learning Numerical Analysis For continuous-time dynamical systems with reversible trajectories, the nowhere-vanishing eigenfunctions of the Koopman operator of the system form a multiplicative group. Here, we exploit this property to accelerate the systematic numerical computation of the eigenspaces of the operator. Given a small set of (so-called ``principal'') eigenfunctions that are approximated conventionally, we can obtain a much larger set by constructing polynomials of the principal eigenfunctions. This enriches the set, and thus allows us to more accurately represent application-specific observables. Often, eigenfunctions exhibit localized singularities (e.g. in simple, one-dimensional problems with multiple steady states) or extended ones (e.g. in simple, two-dimensional problems possessing a limit cycle, or a separatrix); we discuss eigenfunction matching/continuation across such singularities. By handling eigenfunction singularities and enabling their continuation, our approach supports learning consistent global representations from locally sampled data. This is particularly relevant for multistable systems and applications with sparse or fragmented measurements. |
| title | On the algebra of Koopman eigenfunctions and on some of their infinities |
| topic | Dynamical Systems Machine Learning Numerical Analysis |
| url | https://arxiv.org/abs/2604.21825 |