HydroGym: A Multi-Solver Reinforcement Learning Platform for Fluid Dynamics

HydroGym is a multi-solver reinforcement learning platform for data-driven modeling and control of fluid dynamics. This release (v1.0.1) extends HydroGym from a single-solver backend to a native multi-solver reinforcement learning framework for flow control, with environments spanning six CFD solver backends: Firedrake (finite element), MAIA LBM (lattice Boltzmann), MAIA structured FV (finite volume), NEK5000 (spectral element), JAX, and JAX-Fluids (differentiable). It provides 61 pre-configured, Gymnasium-compatible environments ranging from canonical 2D benchmarks to 3D turbulent flows at Reynolds numbers up to 400,000, compatible with standard RL libraries (Stable-Baselines3, RLlib, etc.). This release introduces a data manager abstraction that handles environment data natively and uniformly across solver backends. Accompanying environment checkpoints are hosted in the companion HuggingFace dataset repository (https://huggingface.co/datasets/dynamicslab/HydroGym-environments) and are auto-accessed at runtime. Documentation is available at https://dynamicslab.github.io/hydrogym. This archive corresponds to the software accompanying the associated Nature publication.