mlsanityData to 14 Oct 2026

Emerging trends in machine learning for computational fluid dynamics

arXiv 2211.15145 ↗First posted 28 Nov 2022physics.flu-dyn cs.LGcited by 8

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Abstract (arXiv)

The renewed interest from the scientific community in machine learning (ML) is opening many new areas of research. Here we focus on how novel trends in ML are providing opportunities to improve the field of computational fluid dynamics (CFD). In particular, we discuss synergies between ML and CFD that have already shown benefits, and we also assess areas that are under development and may produce important benefits in the coming years. We believe that it is also important to emphasize a balanced perspective of cautious optimism for these emerging approaches

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 10 of its 35 references that are in the map. First posted on arXiv 28 Nov 2022.

Builds on

10 of 35 references.

References · 10

  1. 2020Neural Operator: Graph Kernel Network for Partial Differential Equationscited by 734
  2. 2021Machine learning accelerated computational fluid dynamicscited by 471
  3. 2019Machine Learning for Fluid Mechanicscited by 385
  4. 2020Discovering Symbolic Models from Deep Learning with Inductive Biasescited by 344
  5. 2021Enhancing Computational Fluid Dynamics with Machine Learningcited by 138
  6. 2021Scientific multi-agent reinforcement learning for wall-models of turbulent flowscited by 52
  7. 2021Data-driven discovery of intrinsic dynamicscited by 45
  8. 2021Towards extraction of orthogonal and parsimonious non-linear modes from turbulent flowscited by 45
  9. 2018Prediction of Reynolds Stresses in High-Mach-Number Turbulent Boundary Layers using Physics-Informed Machine Learningcited by 4
  10. 2021Predicting the near-wall region of turbulence through convolutional neural networkscited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 4; 2025: 0; 2026: 1.

8 papers.

  1. 2024AhmedML: High-Fidelity Computational Fluid Dynamics Dataset for Incompressible, Low-Speed Bluff Body Aerodynamicscited by 15
  2. 2024WindsorML: High-Fidelity Computational Fluid Dynamics Dataset For Automotive Aerodynamicscited by 14
  3. 2023Predictive Limitations of Physics-Informed Neural Networks in Vortex Sheddingcited by 12
  4. 2023A probabilistic, data-driven closure model for RANS simulations with aleatoric, model uncertaintycited by 7
  5. 2024Navigation in a simplified Urban Flow through Deep Reinforcement Learningcited by 5
  6. 2023Physics-informed neural networks modeling for systems with moving immersed boundaries: application to an unsteady flow past a plunging foilcited by 4
  7. 2024Coupling Machine Learning Local Predictions with a Computational Fluid Dynamics Solver to Accelerate Transient Buoyant Plume Simulationscited by 0
  8. 2026Geometry Meets Physics: Data-Efficient Pre-Training for Unstructured Neural PDE Solverscited by 0