mlsanityData to 14 Oct 2026

Physics-guided surrogate learning enables zero-shot control of turbulent wings

arXiv 2604.09434 ↗First posted 10 Apr 2026physics.flu-dyn cs.AIcited by 0

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Abstract (arXiv)

Turbulent boundary layers over aerodynamic surfaces are a major source of aircraft drag, yet their control remains challenging due to multiscale dynamics and spatial variability, particularly under adverse pressure gradients. Reinforcement learning has outperformed state-of-the-art strategies in canonical flows, but its application to realistic geometries is limited by computational cost and transferability. Here we show that these limitations can be overcome by exploiting local structures of wall-bounded turbulence. Policies are trained in turbulent channel flows matched to wing boundary-layer statistics and deployed directly onto a NACA4412 wing section at Rec=2×105 without additional training on the wing, constituting zero-shot deployment. The transferred policies reduce total drag by 11.1%, while state-of-the-art opposition control reduces it by 8.9%. With the actuation cost taken into account, the corresponding net-energy savings are 10.9% and 8.8%, respectively. Surrogate training provides an estimated improvement of four orders of magnitude in exploration efficiency over direct on-wing training, enabling scalable flow control.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 61 references that are in the map. First posted on arXiv 10 Apr 2026.

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7 of 61 references.

References · 7

  1. 2015Continuous control with deep reinforcement learningcited by 3,407
  2. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  3. 2020PettingZoo: Gym for Multi-Agent Reinforcement Learningcited by 220
  4. 2023Identifying regions of importance in wall-bounded turbulence through explainable deep learningcited by 15
  5. 2021Applying Machine Learning to Study Fluid Mechanicscited by 13
  6. 2025Diff-SPORT: Diffusion-based Sensor Placement Optimization and Reconstruction of Turbulent flows in urban environmentscited by 8
  7. 2024Model-based deep reinforcement learning for accelerated learning from flow simulationscited by 4

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  3. 2026Wind-Aware Reinforcement Learning Control of a Small Quadrotor Using Learned Onboard Wind Estimation in Simulated Atmospheric Turbulence
  4. 2026Reward hacking in physical reinforcement learning revealed by turbulent drag reduction
  5. 2025Shocks Under Control: Taming Transonic Compressible Flow over an RAE2822 Airfoil with Deep Reinforcement Learning
  6. 2024Model-Based Reinforcement Learning for Control of Strongly-Disturbed Unsteady Aerodynamic Flows
  7. 2026Gradient-free learning of a closed-loop wall controller for turbulent drag reduction
  8. 2025Attention on flow control: transformer-based reinforcement learning for lift regulation in highly disturbed flows