Physics-informed Neural-operator Predictive Control for Drag Reduction in Turbulent Flows

2025/10/03 by Zelin Zhao, Zhao, Zelin, Zongyi Li +13
Engineering · Physics and Astronomy · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #FOS: Mathematics #FOS: Physical sciences #Fluid Dynamics (physics.flu-dyn) #Fluid Dynamics and Turbulent Flows #Machine Learning (cs.LG) #Model Reduction and Neural Networks #Optimization and Control (math.OC) #Plasma and Flow Control in Aerodynamics

paper · pdf · doi:10.48550/arxiv.2510.03360

openalex publication_date 2025/10/03 · openalex created_date 2025/10/09 · openalex updated_date 2026/07/28

Abstract

Assessing turbulence control effects for wall friction numerically is a significant challenge since it requires expensive simulations of turbulent fluid dynamics. We instead propose an efficient deep reinforcement learning (RL) framework for modeling and control of turbulent flows. It is model-based RL for predictive control (PC), where both the policy and the observer models for turbulence control are learned jointly using Physics Informed Neural Operators (PINO), which are discretization invariant and can capture fine scales in turbulent flows accurately. Our PINO-PC outperforms prior model-free reinforcement learning methods in various challenging scenarios where the flows are of high Reynolds numbers and unseen, i.e., not provided during model training. We find that PINO-PC achieves a drag reduction of 39.0% under a bulk-velocity Reynolds number of 15,000, outperforming previous fluid control methods by more than 32%.

Citations

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Related

• Deep reinforcement learning for turbulent drag reduction in channel flows
• Drag-reduction strategies in wall-bounded turbulent flows using deep reinforcement learning
• Turbulence control in plane Couette flow using low-dimensional neural ODE-based models and deep reinforcement learning
• Improving turbulence control through explainable deep learning
• Reinforcement learning of control strategies for reducing skin friction drag in a fully developed turbulent channel flow