An international team launched HydroGym, a platform for training AI to control fluid flows.
It reduced trial-and-error in reinforcement learning strategies by up to 65%.
The platform includes over 60 environments and is freely available on GitHub.
What happened
An international team launched HydroGym, a platform to train and compare machine learning models for actively controlling fluid flows. In one demonstration, it cut trial-and-error in reinforcement learning control strategies by up to 65%.
Why it matters
Fluid flows underpin several trillion-dollar industries like energy, transportation, health, and defense. Better flow control could reduce drag on airplanes and wind turbines, quiet jet engines, and cool supercomputers, with significant economic and ecological impact.
What to watch
The platform offers over 60 testing environments and is freely available on GitHub. The team is actively growing it with the wider fluid-dynamics community, aiming for a single model of fluid dynamics applicable across turbulence levels, surface shapes, and fluid types.
Ask the AI about this article →
The launch of HydroGym represents a step towards systematic development of flow control models. Traditional approaches often develop controllers for isolated problems without a common framework for comparison, limiting progress. HydroGym addresses this by providing a level playing field for comparing control strategies across different geometries and flow types, with more than 60 environments for training and evaluation.
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