Flow Gym

Francesco Banelli, Antonio Terpin, Alan Bonomi, Raffaello D'Andrea
12/12/2025
physics.flu-dyncs.CVcs.SE

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Abstract

Flow Gym is a toolkit for research and deployment of flow-field quantification methods inspired by OpenAI Gym and Stable-Baselines3. It uses SynthPix as synthetic image generation engine and provides a unified interface for the testing, deployment and training of (learning-based) algorithms for flow-field quantification from a number of consecutive images of tracer particles. It also contains a growing number of integrations of existing algorithms and stable (re-)implementations in JAX.

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Cite this paper

@article{banelli2025flow,
  title  = {Flow Gym},
  author = {Francesco Banelli and Antonio Terpin and Alan Bonomi and Raffaello D'Andrea},
  year   = {2025},
  eprint = {2512.20642},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2512.20642v1}
}

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