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A data-driven adversarial machine learning for 3D surrogates of unstructured computational fluid dynamic simulations

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  • Quilodrán-Casas, César
  • Arcucci, Rossella

Abstract

This paper presents a general workflow to generate and improve the forecast of model surrogates of computational fluid dynamics simulations using deep learning, and most specifically adversarial training. This adversarial approach aims to reduce the divergence of the forecasts from the underlying physical model. Our two-step method integrates a Principal Components Analysis (PCA) based adversarial autoencoder (PC-AAE) with adversarial Long short-term memory (LSTM) networks. Once the reduced-order model (ROM) of the CFD solution is obtained via PCA, an adversarial autoencoder is used on the principal components time series. Subsequentially, a LSTM is adversarially trained on the latent space produced by the PC-AAE to make forecasts. Here we show, that the application of adversarial training improves the rollout of the latent space predictions. Our workflow is applied to three different case studies including two models of urban air pollution in London.

Suggested Citation

  • Quilodrán-Casas, César & Arcucci, Rossella, 2023. "A data-driven adversarial machine learning for 3D surrogates of unstructured computational fluid dynamic simulations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 615(C).
    • Handle: RePEc:eee:phsmap:v:615:y:2023:i:c:s037843712300119x
    DOI: 10.1016/j.physa.2023.128564
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    References listed on IDEAS

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    1. Claire E. Heaney & Andrew G. Buchan & Christopher C. Pain & Simon Jewer, 2021. "Reduced-Order Modelling Applied to the Multigroup Neutron Diffusion Equation Using a Nonlinear Interpolation Method for Control-Rod Movement," Energies, MDPI, vol. 14(5), pages 1-27, March.
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    1. Toby R. F. Phillips & Claire E. Heaney & Brendan S. Tollit & Paul N. Smith & Christopher C. Pain, 2021. "Reduced-Order Modelling with Domain Decomposition Applied to Multi-Group Neutron Transport," Energies, MDPI, vol. 14(5), pages 1-25, March.

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