# Shaping Wind-Tunnel Airflow for Unmanned Aerial Vehicles using Online Learning

arXiv: https://arxiv.org/abs/2608.03378 · HTML: https://mlsanity.com/paper/2608.03378/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 33 references that are in the map.
- First posted on arXiv 4 Aug 2026.

## Builds on (most cited first, 9 of 9)

- [Physics-informed neural networks (PINNs) for fluid mechanics: A review](https://mlsanity.com/paper/2105.09506/) (2021)
- [Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations](https://mlsanity.com/paper/1711.10561/) (2017)
- [Deep Learning of Vortex Induced Vibrations](https://mlsanity.com/paper/1808.08952/) (2018)
- [Physics-informed Autoencoders for Lyapunov-stable Fluid Flow Prediction](https://mlsanity.com/paper/1905.10866/) (2019)
- [Stochastic Online Optimization for Cyber-Physical and Robotic Systems](https://mlsanity.com/paper/2404.05318/) (2024)
- [A Fast and Reliable Pick-and-Place Application with a Spherical Soft Robotic Arm](https://mlsanity.com/paper/2011.04624/) (2020)
- [Data-Efficient Online Learning of Ball Placement in Robot Table Tennis](https://mlsanity.com/paper/2308.14562/) (2023)
- [Embodied Intelligence for Sustainable Flight: A Soaring Robot with Active Morphological Control](https://mlsanity.com/paper/2508.19684/) (2025)
- [Nonholonomic Yaw Control of an Underactuated Flying Robot with Model-based Reinforcement Learning](https://mlsanity.com/paper/2009.01221/) (2020)

## Abstract (arXiv)

The development and testing of advanced aerial robots require experiments in controlled environments with tailored airflow profiles. This paper presents an online learning algorithm for controlling the complex airflow field in a multi-fan vertical wind tunnel. Our method combines a simplified physical model with iterative, measurement-based learning, enabling sample-efficient convergence to desired airflow distributions. We demonstrate the method's versatility by generating complex airflow, such as uniform, Gaussian, and parabolic profiles. Crucially, we show that our algorithm can produce an airflow profile specifically designed for passive soaring, greatly enhancing flight performance of a soaring robot. Variability, practical utility, and robustness of our approach are further highlighted by successful operation with a varying number of fans.