Flow Gym
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.
Authors
- Alan Bonomi
- Raffaello D’Andrea (ORCID: https://orcid.org/0000-0001-5287-7849)
- Antonio Terpin (ORCID: https://orcid.org/0000-0002-8456-6786)
- Francesco Banelli
Publication Details
- Journal
- Repository for Publications and Research Data (ETH Zurich)
- Published
- 2025-12-12
- DOI
- https://doi.org/10.3929/ethz-c-000790942
- Primary Topic
- Medical Image Segmentation Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00