mlsanityData to 7 Oct 2026

Physics-Informed CNNs for Super-Resolution of Sparse Observations on Dynamical Systems

arXiv 2210.17319 ↗First posted 31 Oct 2022physics.flu-dyn cs.LGcited by 8

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Abstract (arXiv)

In the absence of high-resolution samples, super-resolution of sparse observations on dynamical systems is a challenging problem with wide-reaching applications in experimental settings. We showcase the application of physics-informed convolutional neural networks for super-resolution of sparse observations on grids. Results are shown for the chaotic-turbulent Kolmogorov flow, demonstrating the potential of this method for resolving finer scales of turbulence when compared with classic interpolation methods, and thus effectively reconstructing missing physics.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 8 of its 16 references that are in the map. First posted on arXiv 31 Oct 2022.

Builds on

8 of 16 references.

References · 8

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  2. 2017Visualizing the Loss Landscape of Neural Netscited by 1,126
  3. 2015Accurate Image Super-Resolution Using Very Deep Convolutional Networkscited by 902
  4. 2021Physics-informed neural networks (PINNs) for fluid mechanics: A reviewcited by 470
  5. 2020Lagrangian Neural Networkscited by 388
  6. 2019Machine Learning for Fluid Mechanicscited by 380
  7. 2018Deep Learning for Single Image Super-Resolution: A Brief Reviewcited by 158
  8. 2022Physics-informed deep-learning applications to experimental fluid mechanicscited by 21

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Cited by year (2026 to 7 Oct)

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

8 papers.

  1. 2023Physics-Informed Computer Vision: A Review and Perspectivescited by 28
  2. 2025Multimodal Atmospheric Super-Resolution With Deep Generative Modelscited by 8
  3. 2023Reconstruction, forecasting, and stability of chaotic dynamics from partial datacited by 7
  4. 2023Learning the solution operator of two-dimensional incompressible Navier-Stokes equations using physics-aware convolutional neural networkscited by 6
  5. 2024Thermodynamics-informed super-resolution of scarce temporal dynamics datacited by 3
  6. 2024Reconstructing unsteady flows from sparse, noisy measurements with a physics-constrained convolutional neural networkcited by 2
  7. 2024How to Re-enable PDE Loss for Physical Systems Modeling Under Partial Observationcited by 0
  8. 2026Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matchingcited by 0