mlsanityData to 13 Oct 2026

The transformative potential of machine learning for experiments in fluid mechanics

arXiv 2303.15832 ↗First posted 28 Mar 2023physics.flu-dyn cs.AIcited by 26

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Abstract (arXiv)

The field of machine learning has rapidly advanced the state of the art in many fields of science and engineering, including experimental fluid dynamics, which is one of the original big-data disciplines. This perspective will highlight several aspects of experimental fluid mechanics that stand to benefit from progress advances in machine learning, including: 1) augmenting the fidelity and quality of measurement techniques, 2) improving experimental design and surrogate digital-twin models and 3) enabling real-time estimation and control. In each case, we discuss recent success stories and ongoing challenges, along with caveats and limitations, and outline the potential for new avenues of ML-augmented and ML-enabled experimental fluid mechanics.

Key facts. Cited by 26 papers in the map (data through 13 Oct 2026). Builds on 27 of its 148 references that are in the map. First posted on arXiv 28 Mar 2023.

Builds on

27 of 148 references.

References · 27

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  2. 2022Generative Adversarial Networkscited by 5,614
  3. 2018Neural Ordinary Differential Equationscited by 3,750
  4. 2019DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operatorscited by 1,989
  5. 2020Neural Operator: Graph Kernel Network for Partial Differential Equationscited by 728
  6. 2020Lagrangian Neural Networkscited by 392
  7. 2019Machine Learning for Fluid Mechanicscited by 381
  8. 2020Discovering Symbolic Models from Deep Learning with Inductive Biasescited by 339
  9. 2018Data-Driven Forecasting of High-Dimensional Chaotic Systems with Long Short-Term Memory Networkscited by 155
  10. 2021Enhancing Computational Fluid Dynamics with Machine Learningcited by 137
  11. 2018A Tour of Reinforcement Learning: The View from Continuous Controlcited by 130
  12. 2018Efficient collective swimming by harnessing vortices through deep reinforcement learningcited by 73
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  1. 2018Deep Dynamical Modeling and Control of Unsteady Fluid Flowscited by 67
  2. 2018Shared Autonomy via Deep Reinforcement Learningcited by 65
  3. 2020A Probabilistic Graphical Model Foundation for Enabling Predictive Digital Twins at Scalecited by 58
  4. 2017Data-Driven Sparse Sensor Placement for Reconstructioncited by 57
  5. 2021Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learningcited by 56
  6. 2019Learning Symbolic Physics with Graph Networkscited by 52
  7. 2021Scientific multi-agent reinforcement learning for wall-models of turbulent flowscited by 52
  8. 2020Convolutional-network models to predict wall-bounded turbulence from wall quantitiescited by 46
  9. 2020Data-Driven Aerospace Engineering: Reframing the Industry with Machine Learningcited by 27
  10. 2021Learning Efficient Navigation in Vortical Flow Fieldscited by 26
  11. 2022Physics-informed deep-learning applications to experimental fluid mechanicscited by 21
  12. 2021Interpretable deep-learning models to help achieve the Sustainable Development Goalscited by 18
  13. 2019Seeing the Wind: Visual Wind Speed Prediction with a Coupled Convolutional and Recurrent Neural Networkcited by 7
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Citing papers per year: 2023: 3; 2024: 11; 2025: 9; 2026: 3.

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  11. 2025Improving the Robustness of Control of Chaotic Convective Flows with Domain-Informed Reinforcement Learningcited by 0
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