Physics-Driven Convolutional Autoencoder Approach for CFD Data Compressions: arXiv:2210.09262 [physics.flu-dyn]

Alberto Olmo, Ahmed Zamzam, Andrew Glaws, Ryan King

Research output: Contribution to journalArticle


With the growing size and complexity of turbulent flow models, data compression approaches are of the utmost importance to analyze, visualize, or restart the simulations. Recently, in-situ autoencoder-based compression approaches have been proposed and shown to be effective at producing reduced representations of turbulent flow data. However, these approaches focus solely on training the model using point-wise sample reconstruction losses that do not take advantage of the physical properties of turbulent flows. In this paper, we show that training autoencoders with additional physics-informed regularizations, e.g., enforcing incompressibility and preserving enstrophy, improves the compression model in three ways: (i) the compressed data better conform to known physics for homogeneous isotropic turbulence without negatively impacting point-wise reconstruction quality, (ii) inspection of the gradients of the trained model uncovers changes to the learned compression mapping that can facilitate the use of explainability techniques, and (iii) as a performance byproduct, training losses are shown to converge up to 12x faster than the baseline model.
Original languageAmerican English
Number of pages6
StatePublished - 2022

NREL Publication Number

  • NREL/JA-5D00-84722


  • autoencoders
  • turbulent flow


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