mlsanityData to 7 Oct 2026

Grasping Extreme Aerodynamics on a Low-Dimensional Manifold

arXiv 2305.08024 ↗First posted 13 May 2023physics.flu-dyn cs.LGcited by 18

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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.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 3 of its 72 references that are in the map. First posted on arXiv 13 May 2023.

Builds on

3 of 72 references.

References · 3

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2021Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learningcited by 55
  3. 2022Data-driven low-dimensional dynamic model of Kolmogorov flowcited by 9

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 6; 2025: 8; 2026: 4.

18 papers.

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  1. 2024Generative prediction of flow fields around an obstacle using the diffusion modelcited by 6
  2. 2024Dynamical system prediction from sparse observations using deep neural networks with Voronoi tessellation and physics constraintcited by 5
  3. 2025Low-Order Flow Reconstruction and Uncertainty Quantification in Disturbed Aerodynamics Using Sparse Pressure Measurementscited by 4
  4. 2024Decoder Decomposition for the Analysis of the Latent Space of Nonlinear Autoencoders With Wind-Tunnel Experimental Datacited by 3
  5. 2024Model-Based Reinforcement Learning for Control of Strongly-Disturbed Unsteady Aerodynamic Flowscited by 2
  6. 2025Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Controlcited by 2
  7. 2025Differentiable Autoencoding Neural Operator for Interpretable and Integrable Latent Space Modelingcited by 2
  8. 2025Capturing Extreme Events in Turbulence using an Extreme Variational Autoencoder (xVAE)cited by 1
  9. 2025Multi-resolution Physics-Aware Recurrent Convolutional Neural Network for Complex Flowscited by 1
  10. 2024Phase autoencoder for limit-cycle oscillatorscited by 0
  11. 2024Parametric Taylor series based latent dynamics identification neural networkscited by 0
  12. 2025Fourier-Invertible Neural Encoder (FINE) for Homogeneous Flowscited by 0
6 more
  1. 2025Information-theoretic machine learning for time-varying mode decomposition of separated aerodynamic flowscited by 0
  2. 2025Attention on flow control: transformer-based reinforcement learning for lift regulation in highly disturbed flowscited by 0
  3. 2026Spatiotemporal System Forecasting with Irregular Time Steps via Masked Autoencodercited by 0
  4. 2026Uncovering Extreme Event Mechanisms for Prediction and Control with Sensitivity-Balanced Projectionscited by 0
  5. 2026Variational Parameter Calibration with Physics-Aware Latent-Space Surrogatescited by 0
  6. 2026Exemplar-based objective classification of gust-induced loads across multiple flight conditionscited by 0