kooplearn: A scikit-learn compatible library of algorithms for evolution operator learning

kooplearn: A scikit-learn compatible library of algorithms for evolution operator learning - Published in JOSS (2026)

https://github.com/machine-learning-dynamical-systems/kooplearn

Science Score: 87.0%

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    Found 1 DOI reference(s) in JOSS metadata
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    Published in Journal of Open Source Software
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JOSS Publication

kooplearn: A scikit-learn compatible library of algorithms for evolution operator learning
Published
June 25, 2026
Volume 11, Issue 122, Page 10342
Authors
Giacomo Turri ORCID
Computational Statistics and Machine Learning, Italian Institute of Technology, Genoa, Italy
Grégoire Pacreau
Centre de Mathématiques Appliquées, École Polytechnique, Palaiseau, France
Giacomo Meanti ORCID
Centre Inria de l’Université Grenoble Alpes, Montbonnot, France
Timothée Devergne ORCID
Atomistic Simulations, Italian Institute of Technology, Genoa, Italy, Computational Statistics and Machine Learning, Italian Institute of Technology, Genoa, Italy
Daniel Ordoñez-Apraez ORCID
Computational Statistics and Machine Learning, Italian Institute of Technology, Genoa, Italy
Erfan Mirzaei ORCID
Computational Statistics and Machine Learning, Italian Institute of Technology, Genoa, Italy
Bruno Belucci
Paris Dauphine University, Paris, France
Karim Lounici ORCID
Centre de Mathématiques Appliquées, École Polytechnique, Palaiseau, France
Vladimir R. Kostic ORCID
Computational Statistics and Machine Learning, Italian Institute of Technology, Genoa, Italy, Department of Mathematics and Informatics, Faculty of Science, University of Novi Sad, Novi Sad, Serbia
Massimiliano Pontil ORCID
Computational Statistics and Machine Learning, Italian Institute of Technology, Genoa, Italy, AI Centre, Department of Computer Science, University College London, London, UK
Pietro Novelli ORCID
Computational Statistics and Machine Learning, Italian Institute of Technology, Genoa, Italy
Editor
Johan Larsson ORCID
Tags
dynamical systems evolution operator koopman operator transfer operator operator learning machine learning representation learning

Issues and Pull Requests

Last synced: 3 months ago

All Time
  • Total issues: 5
  • Total pull requests: 20
  • Average time to close issues: over 1 year
  • Average time to close pull requests: 7 days
  • Total issue authors: 3
  • Total pull request authors: 6
  • Average comments per issue: 1.6
  • Average comments per pull request: 0.45
  • Merged pull requests: 14
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 4
  • Average time to close issues: N/A
  • Average time to close pull requests: 10 days
  • Issue authors: 0
  • Pull request authors: 2
  • Average comments per issue: 0
  • Average comments per pull request: 0.5
  • Merged pull requests: 4
  • Bot issues: 0
  • Bot pull requests: 0
Top Authors
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  • pietronvll (2)
  • Danfoa (2)
  • Giodiro (1)
Pull Request Authors
  • pietronvll (7)
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  • g-turri (4)
  • Giodiro (2)
  • mitchellostrow (2)
  • DevergneTimothee (1)
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Packages

  • Total packages: 1
  • Total downloads:
    • pypi 304 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 2
  • Total versions: 54
  • Total maintainers: 1
pypi.org: kooplearn

A python package for evolution operator learning

  • Versions: 54
  • Dependent Packages: 0
  • Dependent Repositories: 2
  • Downloads: 304 Last month
Rankings
Dependent packages count: 10.1%
Dependent repos count: 11.5%
Stargazers count: 15.2%
Average: 15.6%
Forks count: 19.1%
Downloads: 21.7%
Maintainers (1)
Last synced: 28 days ago