mlsanityData to 14 Oct 2026

Generative Adversarial Reduced Order Modelling

arXiv 2305.15881 ↗First posted 25 May 2023cs.LG cs.NA math.NAcited by 7

Not on the map. Why?

Abstract (arXiv)

In this work, we present GAROM, a new approach for reduced order modelling (ROM) based on generative adversarial networks (GANs). GANs have the potential to learn data distribution and generate more realistic data. While widely applied in many areas of deep learning, little research is done on their application for ROM, i.e. approximating a high-fidelity model with a simpler one. In this work, we combine the GAN and ROM framework, by introducing a data-driven generative adversarial model able to learn solutions to parametric differential equations. The latter is achieved by modelling the discriminator network as an autoencoder, extracting relevant features of the input, and applying a conditioning mechanism to the generator and discriminator networks specifying the differential equation parameters. We show how to apply our methodology for inference, provide experimental evidence of the model generalisation, and perform a convergence study of the method.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 39 of its 80 references that are in the map. First posted on arXiv 25 May 2023.

Builds on

39 of 80 references.

References · 39

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27 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 1; 2026: 4.

7 papers.

  1. 2024FUSE: Fast Unified Simulation and Estimation for PDEscited by 8
  2. 2025BARNN: A Bayesian Autoregressive and Recurrent Neural Networkcited by 5
  3. 2024Machine Learning Applications to Computational Plasma Physics and Reduced-Order Plasma Modeling: A Perspectivecited by 4
  4. 2026Surrogates for Physics-based and Data-driven Modelling of Parametric Systems: Review and New Perspectivescited by 2
  5. 2026PODiff: Latent Diffusion in Proper Orthogonal Decomposition Space for Scientific Super-Resolutioncited by 2
  6. 2026Patch-PODiff-ViT: Structured Latent Diffusion with Patchwise POD for Super-Resolution and Uncertainty Quantificationcited by 1
  7. 2026Advances in Scientific Machine Learning for Coupled Fluid Flow and Transportcited by 0