Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control
arXiv:2601.15015v2 Announce Type: replace Abstract: Reinforcement learning (RL) has shown promising results in active flow control (AFC), yet progress in the field remains difficult to assess as existing studies rely on heterogeneous observation and actuation schemes, numerical setups, and evaluation protocols. Current AFC benchmarks attempt to address these issues but heavily rely on external computational fluid dynamics (CFD) solvers, are not fully differentiable, and provide limited 3D and multi-agent support. To overcome these limitations, we introduce FluidGym, the first standalone, fully
The proliferation of AI and RL in complex engineering domains necessitates better, standardized benchmarking tools to accelerate development and deployment.
A standalone, differentiable, 3D, and multi-agent capable RL benchmark for flow control will significantly advance AI applications in fields like aerospace, climate modeling, and industrial processes.
The introduction of FluidGym provides a unified and more advanced platform for evaluating RL algorithms in active flow control, reducing reliance on heterogeneous and limited existing methods.
- · AI/ML researchers
- · Fluid dynamics engineers
- · Aerospace industry
- · HVAC industry
- · Developers of proprietary, non-differentiable CFD solvers for RL
Faster development and optimization of AI-driven flow control solutions for various applications.
Improved energy efficiency and performance in systems relying on fluid dynamics, such as aircraft, wind turbines, and industrial pipelines.
Potential for new materials and design paradigms leveraging highly optimized AI-driven flow control for extreme environments.
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Read at arXiv cs.LG