mlsanityData to 7 Oct 2026

Super-Resolution Analysis via Machine Learning: A Survey for Fluid Flows

arXiv 2301.10937 ↗First posted 26 Jan 2023physics.flu-dyn cs.LG physics.comp-phcited by 30

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Abstract (arXiv)

This paper surveys machine-learning-based super-resolution reconstruction for vortical flows. Super resolution aims to find the high-resolution flow fields from low-resolution data and is generally an approach used in image reconstruction. In addition to surveying a variety of recent super-resolution applications, we provide case studies of super-resolution analysis for an example of two-dimensional decaying isotropic turbulence. We demonstrate that physics-inspired model designs enable successful reconstruction of vortical flows from spatially limited measurements. We also discuss the challenges and outlooks of machine-learning-based super-resolution analysis for fluid flow applications. The insights gained from this study can be leveraged for super-resolution analysis of numerical and experimental flow data.

Key facts. Cited by 30 papers in the map (data through 7 Oct 2026). Builds on 39 of its 191 references that are in the map. First posted on arXiv 26 Jan 2023.

Builds on

39 of 191 references.

References · 39

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27 more
  1. 2021Machine learning accelerated computational fluid dynamicscited by 461
  2. 2016Accelerating the Super-Resolution Convolutional Neural Networkcited by 393
  3. 2019Machine Learning for Fluid Mechanicscited by 380
  4. 2020PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parameterized Steady-State PDEs on Irregular Domaincited by 173
  5. 2018Deep Learning for Single Image Super-Resolution: A Brief Reviewcited by 158
  6. 2020MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Frameworkcited by 78
  7. 2019Diversity-Sensitive Conditional Generative Adversarial Networkscited by 77
  8. 2017Data-Driven Sparse Sensor Placement for Reconstructioncited by 57
  9. 2021Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learningcited by 55
  10. 2021Scientific multi-agent reinforcement learning for wall-models of turbulent flowscited by 52
  11. 2021Meta-learning PINN loss functionscited by 51
  12. 2020Convolutional-network models to predict wall-bounded turbulence from wall quantitiescited by 46
  13. 2020A Point-Cloud Deep Learning Framework for Prediction of Fluid Flow Fields on Irregular Geometriescited by 44
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  20. 2021Adversarial sampling of unknown and high-dimensional conditional distributionscited by 13
  21. 2021Deep Hierarchical Super Resolution for Scientific Datacited by 12
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 5; 2024: 13; 2025: 8; 2026: 4.

30 papers.

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  1. 2023SuperBench: A Super-Resolution Benchmark Dataset for Scientific Machine Learningcited by 18
  2. 2024Spatially-Aware Diffusion Models with Cross-Attention for Global Field Reconstruction with Sparse Observationscited by 13
  3. 2025Machine learning for modelling unstructured grid data in computational physics: a reviewcited by 12
  4. 2025Guiding diffusion models to reconstruct flow fields from sparse datacited by 11
  5. 2024Mesh-based Super-Resolution of Fluid Flows with Multiscale Graph Neural Networkscited by 10
  6. 2023Interpreting and generalizing deep learning in physics-based problems with functional linear modelscited by 9
  7. 2024Single-snapshot machine learning for super-resolution of turbulencecited by 8
  8. 2025Multimodal Atmospheric Super-Resolution With Deep Generative Modelscited by 8
  9. 2024Observation-Guided Meteorological Field Downscaling at Station Scale: A Benchmark and a New Methodcited by 7
  10. 2025VideoPDE: Unified Generative PDE Solving via Video Inpainting Diffusion Modelscited by 7
  11. 2024Multimodal Super-Resolution: Discovering hidden physics and its application to fusion plasmascited by 5
  12. 2023Ensemble flow reconstruction in the atmospheric boundary layer from spatially limited measurements through latent diffusion modelscited by 4
18 more
  1. 2023Physics-informed neural networks modeling for systems with moving immersed boundaries: application to an unsteady flow past a plunging foilcited by 4
  2. 2024Thermodynamics-informed super-resolution of scarce temporal dynamics datacited by 3
  3. 2026SIMR-NO: A Spectrally-Informed Multi-Resolution Neural Operator for Turbulent Flow Super-Resolutioncited by 3
  4. 2024Reconstructing unsteady flows from sparse, noisy measurements with a physics-constrained convolutional neural networkcited by 2
  5. 2025Guided Unconditional and Conditional Generative Models for Super-Resolution and Inference of Quasi-Geostrophic Turbulencecited by 2
  6. 2024Dynamic Deep Learning Based Super-Resolution For The Shallow Water Equationscited by 1
  7. 2024Generalization capabilities and robustness of hybrid models grounded in physics compared to purely deep learning modelscited by 1
  8. 2024Physics-aligned Schr"odinger bridgecited by 1
  9. 2025Understanding the role of autoencoders for stiff dynamical systems using information theorycited by 1
  10. 2025Patch-Based 3D Variational Autoencoder for Super-Resolution of Turbulent Channel Flowcited by 1
  11. 2023Enhancing wind field resolution in complex terrain through a knowledge-driven machine learning approachcited by 0
  12. 2024WindSeer: Real-time volumetric wind prediction over complex terrain aboard a small UAVcited by 0
  13. 2024Reducing Spatial Discretization Error on Coarse CFD Simulations Using an OpenFOAM-Embedded Deep Learning Frameworkcited by 0
  14. 2024Upstream flow geometries can be uniquely learnt from single-point turbulence signaturescited by 0
  15. 2025Mesh-based Super-resolution of Multiscale Detonation Flows with Graph Transformerscited by 0
  16. 2026Annealed Co-Generation: Disentangling Variables via Progressive Pairwise Modelingcited by 0
  17. 2026Long-horizon prediction of three-dimensional wall-bounded turbulence with CTA-Swin-UNet and resolvent analysiscited by 0
  18. 2026RECAST: A Machine-Learning Framework for Correction and Super-Resolution of Coarse-Grid PDE Solverscited by 0