# Grasping Extreme Aerodynamics on a Low-Dimensional Manifold

arXiv: https://arxiv.org/abs/2305.08024 · HTML: https://mlsanity.com/paper/2305.08024/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 72 references that are in the map.
- First posted on arXiv 13 May 2023.

## Builds on (most cited first, 3 of 3)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning](https://mlsanity.com/paper/2101.00554/) (2021)
- [Data-driven low-dimensional dynamic model of Kolmogorov flow](https://mlsanity.com/paper/2210.16708/) (2022)

## Built on by (most cited first, 19 of 19)

- [Generative prediction of flow fields around an obstacle using the diffusion model](https://mlsanity.com/paper/2407.00735/) (2024)
- [Dynamical system prediction from sparse observations using deep neural networks with Voronoi tessellation and physics constraint](https://mlsanity.com/paper/2409.00458/) (2024)
- [Low-Order Flow Reconstruction and Uncertainty Quantification in Disturbed Aerodynamics Using Sparse Pressure Measurements](https://mlsanity.com/paper/2501.03406/) (2025)
- [Decoder Decomposition for the Analysis of the Latent Space of Nonlinear Autoencoders With Wind-Tunnel Experimental Data](https://mlsanity.com/paper/2404.19660/) (2024)
- [Model-Based Reinforcement Learning for Control of Strongly-Disturbed Unsteady Aerodynamic Flows](https://mlsanity.com/paper/2408.14685/) (2024)
- [Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control](https://mlsanity.com/paper/2506.20554/) (2025)
- [Differentiable Autoencoding Neural Operator for Interpretable and Integrable Latent Space Modeling](https://mlsanity.com/paper/2510.00233/) (2025)
- [Parametric Taylor series based latent dynamics identification neural networks](https://mlsanity.com/paper/2410.04193/) (2024)
- [Capturing Extreme Events in Turbulence using an Extreme Variational Autoencoder (xVAE)](https://mlsanity.com/paper/2502.04685/) (2025)
- [Multi-resolution Physics-Aware Recurrent Convolutional Neural Network for Complex Flows](https://mlsanity.com/paper/2512.06031/) (2025)
- [Phase autoencoder for limit-cycle oscillators](https://mlsanity.com/paper/2403.06992/) (2024)
- [Fourier-Invertible Neural Encoder (FINE) for Homogeneous Flows](https://mlsanity.com/paper/2505.15329/) (2025)
- [Information-theoretic machine learning for time-varying mode decomposition of separated aerodynamic flows](https://mlsanity.com/paper/2505.24132/) (2025)
- [Attention on flow control: transformer-based reinforcement learning for lift regulation in highly disturbed flows](https://mlsanity.com/paper/2506.10153/) (2025)
- [Spatiotemporal System Forecasting with Irregular Time Steps via Masked Autoencoder](https://mlsanity.com/paper/2603.25597/) (2026)
- [Uncovering Extreme Event Mechanisms for Prediction and Control with Sensitivity-Balanced Projections](https://mlsanity.com/paper/2606.05618/) (2026)
- [Variational Parameter Calibration with Physics-Aware Latent-Space Surrogates](https://mlsanity.com/paper/2608.11435/) (2026)
- [Exemplar-based objective classification of gust-induced loads across multiple flight conditions](https://mlsanity.com/paper/2608.12448/) (2026)
- [Latent-Lagrangian Neural Networks for Reduced Order Modeling of Non-autonomous Nonlinear Dynamical Systems](https://mlsanity.com/paper/2610.04723/) (2026)

## Abstract (arXiv)

Modern air vehicles perform a wide range of operations, including transportation, defense, surveillance, and rescue. These aircraft can fly in calm conditions but avoid operations in gusty environments, encountered in urban canyons, over mountainous terrains, and in ship wakes. With extreme weather becoming ever more frequent due to global warming, it is anticipated that aircraft, especially those that are smaller in size, will encounter sizeable atmospheric disturbances and still be expected to achieve stable flight. However, there exists virtually no theoretical fluid-dynamic foundation to describe the influence of extreme vortical gusts on wings. To compound this difficulty, there is a large parameter space for gust-wing interactions. While such interactions are seemingly complex and different for each combination of gust parameters, we show that the fundamental physics behind extreme aerodynamics is far simpler and lower-rank than traditionally expected. We reveal that the nonlinear vortical flow field over time and parameter space can be compressed to only three variables with a lift-augmented autoencoder while holding the essence of the original high-dimensional physics. Extreme aerodynamic flows can be compressed through machine learning into a low-dimensional manifold, which can enable real-time sparse reconstruction, dynamical modeling, and control of extremely unsteady gusty flows. The present findings offer support for the stable flight of next-generation small air vehicles in atmosphere conditions traditionally considered unflyable.