mlsanityData to 7 Oct 2026

S3RP: Self-Supervised Super-Resolution and Prediction for Advection-Diffusion Process

arXiv 2111.04639 ↗First posted 8 Nov 2021cs.LG cs.CV physics.comp-phcited by 9

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Abstract (arXiv)

We present a super-resolution model for an advection-diffusion process with limited information. While most of the super-resolution models assume high-resolution (HR) ground-truth data in the training, in many cases such HR dataset is not readily accessible. Here, we show that a Recurrent Convolutional Network trained with physics-based regularizations is able to reconstruct the HR information without having the HR ground-truth data. Moreover, considering the ill-posed nature of a super-resolution problem, we employ the Recurrent Wasserstein Autoencoder to model the uncertainty.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 18 of its 21 references that are in the map. First posted on arXiv 8 Nov 2021.

Builds on

18 of 21 references.

References · 18

  1. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  2. 2015Variational Inference with Normalizing Flowscited by 2,161
  3. 2018ESRGAN: Enhanced Super-Resolution Generative Adversarial Networkscited by 1,250
  4. 2014Image Super-Resolution Using Deep Convolutional Networkscited by 1,218
  5. 2017MoCoGAN: Decomposing Motion and Content for Video Generationcited by 523
  6. 2020PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Modelscited by 358
  7. 2020SRFlow: Learning the Super-Resolution Space with Normalizing Flowcited by 209
  8. 2019Towards Physics-informed Deep Learning for Turbulent Flow Predictioncited by 203
  9. 2020Disentangling Physical Dynamics from Unknown Factors for Unsupervised Video Predictioncited by 192
  10. 2015Training generative neural networks via Maximum Mean Discrepancy optimizationcited by 149
  11. 2020MetNet: A Neural Weather Model for Precipitation Forecastingcited by 128
  12. 2017DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolutioncited by 80
6 more
  1. 2020MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Frameworkcited by 78
  2. 2019VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generationcited by 74
  3. 2020S3VAE: Self-Supervised Sequential VAE for Representation Disentanglement and Data Generationcited by 63
  4. 2021Disentangled Recurrent Wasserstein Autoencodercited by 22
  5. 2020Physics-Informed Neural Network Super Resolution for Advection-Diffusion Modelscited by 16
  6. 2018Development of hp-inverse model by using generalized polynomial chaoscited by 3

Built on by

Cited by year

Citing papers per year: 2022: 3; 2023: 5; 2024: 0; 2025: 1.

9 papers.

  1. 2023Consistent View Synthesis with Pose-Guided Diffusion Modelscited by 117
  2. 2022BinauralGrad: A Two-Stage Conditional Diffusion Probabilistic Model for Binaural Audio Synthesiscited by 44
  3. 2023ExposureDiffusion: Learning to Expose for Low-light Image Enhancementcited by 29
  4. 2023CamoDiffusion: Camouflaged Object Detection via Conditional Diffusion Modelscited by 21
  5. 2022Arbitrary Style Guidance for Enhanced Diffusion-Based Text-to-Image Generationcited by 9
  6. 2023ChatTraffic: Text-to-Traffic Generation via Diffusion Modelcited by 7
  7. 2023DiffWA: Diffusion Models for Watermark Attackcited by 3
  8. 2025InpDiffusion: Image Inpainting Localization via Conditional Diffusion Modelscited by 3
  9. 2022Multi-task Learning for Source Attribution and Field Reconstruction for Methane Monitoringcited by 0