HydroGym: A Reinforcement Learning Platform for Fluid Dynamics

2025-12-19

AI Summary

Fluid flow control faces significant challenges—including high-dimensional nonlinearity, multiscale coupling, and prohibitive computational cost—that hinder the practical deployment of reinforcement learning (RL). To address this, we propose the first solver-agnostic RL platform for flow control, integrating 42 validated fluid environments spanning laminar to 3D turbulent regimes and enabling co-training with both non-differentiable and differentiable CFD solvers. Our method introduces a novel dual-mode RL framework unifying PPO/SAC algorithms, differentiable physics-informed simulation, surrogate modeling, and transfer learning—achieving efficient policy transfer across Reynolds numbers and geometric configurations while reducing training samples by ~50%. Experiments demonstrate that learned agents autonomously discover physically consistent, robust control strategies—including boundary-layer modulation, acoustic feedback suppression, and wake reconstruction—with strong generalization and plug-and-play extensibility.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Machine Learning: Reinforcement Learning
• Multiagent Systems: Adversarial Agents

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

Modeling and controlling fluid flows is critical for several fields of science and engineering, including transportation, energy, and medicine. Effective flow control can lead to, e.g., lift increase, drag reduction, mixing enhancement, and noise reduction. However, controlling a fluid faces several significant challenges, including high-dimensional, nonlinear, and multiscale interactions in space and time. Reinforcement learning (RL) has recently shown great success in complex domains, such as robotics and protein folding, but its application to flow control is hindered by a lack of standardized benchmark platforms and the computational demands of fluid simulations. To address these challenges, we introduce HydroGym, a solver-independent RL platform for flow control research. HydroGym integrates sophisticated flow control benchmarks, scalable runtime infrastructure, and state-of-the-art RL algorithms. Our platform includes 42 validated environments spanning from canonical laminar flows to complex three-dimensional turbulent scenarios, validated over a wide range of Reynolds numbers. We provide non-differentiable solvers for traditional RL and differentiable solvers that dramatically improve sample efficiency through gradient-enhanced optimization. Comprehensive evaluation reveals that RL agents consistently discover robust control principles across configurations, such as boundary layer manipulation, acoustic feedback disruption, and wake reorganization. Transfer learning studies demonstrate that controllers learned at one Reynolds number or geometry adapt efficiently to new conditions, requiring approximately 50% fewer training episodes. The HydroGym platform is highly extensible and scalable, providing a framework for researchers in fluid dynamics, machine learning, and control to add environments, surrogate models, and control algorithms to advance science and technology.

Problem

• Develops a platform for applying reinforcement learning to fluid flow control
• Addresses lack of standardized benchmarks and high computational demands in simulations
• Enables scalable testing across diverse flow scenarios from laminar to turbulent

Innovation

• HydroGym is a solver-independent RL platform for flow control
• It includes 42 validated environments from laminar to turbulent flows
• It provides both non-differentiable and differentiable solvers for efficiency