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

arXiv: https://arxiv.org/abs/2604.09434 · HTML: https://mlsanity.com/paper/2604.09434/

## 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.

## Builds on (most cited first, 7 of 7)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [PettingZoo: Gym for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2009.14471/) (2020)
- [Identifying regions of importance in wall-bounded turbulence through explainable deep learning](https://mlsanity.com/paper/2302.01250/) (2023)
- [Applying Machine Learning to Study Fluid Mechanics](https://mlsanity.com/paper/2110.02083/) (2021)
- [Diff-SPORT: Diffusion-based Sensor Placement Optimization and Reconstruction of Turbulent flows in urban environments](https://mlsanity.com/paper/2506.00214/) (2025)
- [Model-based deep reinforcement learning for accelerated learning from flow simulations](https://mlsanity.com/paper/2402.16543/) (2024)

## 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.