mlsanityData to 14 Oct 2026

Generative Network-Based Reduced-Order Model for Prediction, Data Assimilation and Uncertainty Quantification

arXiv 2105.13859 ↗First posted 28 May 2021cs.LG stat.MLcited by 2

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Abstract (arXiv)

We propose a new method in which a generative network (GN) integrate into a reduced-order model (ROM) framework is used to solve inverse problems for partial differential equations (PDE). The aim is to match available measurements and estimate the corresponding uncertainties associated with the states and parameters of a numerical physical simulation. The GN is trained using only unconditional simulations of the discretized PDE model. We compare the proposed method with the golden standard Markov chain Monte Carlo. We apply the proposed approaches to a spatio-temporal compartmental model in epidemiology. The results show that the proposed GN-based ROM can efficiently quantify uncertainty and accurately match the measurements and the golden standard, using only a few unconditional simulations of the full-order numerical PDE model.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 20 of its 86 references that are in the map. First posted on arXiv 28 May 2021.

Builds on

20 of 86 references.

References · 20

  1. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
  2. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  3. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,588
  4. 2017Improved Training of Wasserstein GANscited by 2,235
  5. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,054
  6. 2020Learning to Simulate Complex Physics with Graph Networkscited by 880
  7. 2015Automatic differentiation in machine learning: a surveycited by 855
  8. 2017PDE-Net: Learning PDEs from Datacited by 311
  9. 2021Skillful Precipitation Nowcasting using Deep Generative Models of Radarcited by 311
  10. 2016NIPS 2016 Tutorial: Generative Adversarial Networkscited by 285
  11. 2018GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classificationcited by 242
  12. 2019Towards Physics-informed Deep Learning for Turbulent Flow Predictioncited by 204
8 more
  1. 2017CycleGAN, a Master of Steganographycited by 65
  2. 2019A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problemscited by 59
  3. 2021A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networkscited by 30
  4. 2020An autoencoder-based reduced-order model for eigenvalue problems with application to neutron diffusioncited by 19
  5. 2020Data-driven modelling of nonlinear spatio-temporal fluid flows using a deep convolutional generative adversarial networkcited by 16
  6. 2020Recent Developments Combining Ensemble Smoother and Deep Generative Networks for Facies History Matchingcited by 8
  7. 2021Digital twins based on bidirectional LSTM and GAN for modelling the COVID-19 pandemiccited by 5
  8. 2021Data Assimilation Predictive GAN (DA-PredGAN): applied to determine the spread of COVID-19cited by 4

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Cited by year

Citing papers per year: 2023: 1; 2024: 1.

2 papers.

  1. 2023Generative Adversarial Reduced Order Modellingcited by 7
  2. 2024The Deep Latent Space Particle Filter for Real-Time Data Assimilation with Uncertainty Quantificationcited by 3