ZipGAN: Super-Resolution-based Generative Adversarial Network Framework for Data Compression of Direct Numerical Simulations

arXiv (Cornell University)

Abstract

The advancement of high-performance computing has enabled the generation of large direct numerical simulation (DNS) datasets of turbulent flows, driving the need for efficient compression/decompression techniques that reduce storage demands while maintaining fidelity. Traditional methods, such as the discrete wavelet transform (DWT), cannot achieve compression ratios of 8 or higher for complex turbulent flows without introducing significant encoding/decoding errors. On the other hand, a super-resolution-based generative adversarial network (SR-GAN), called ZipGAN, can accurately reconstruct fine-scale features, preserving velocity gradients and structural details, even at a compression ratio of 512, thanks to the more efficient representation of the data in a compact latent space. Additional benefits are ascribed to adversarial training. The high GAN training time is significantly reduced with a progressive transfer learning approach and, once trained, they can be applied independently of the Reynolds number. It is demonstrated that ZipGAN can enhance dataset temporal resolution without additional simulation overhead by generating high-quality intermediate fields from compressed snapshots. The ZipGAN discriminator can reliably evaluate the quality of decoded fields, ensuring fidelity even in the absence of original DNS fields. Hence, ZipGAN compression/decompression method presents a highly efficient and scalable alternative for large-scale DNS storage and transfer, offering substantial advantages over the DWT methods in terms of compression efficiency, reconstruction fidelity, and temporal resolution enhancement.

Authors 8

1. Ludovico Nista Aachen Institute for Combustion Technology

   RWTH Aachen University

   Affiliation as printed

   Institute for Combustion Technology , RWTH Aachen University , Aachen , 52056 , Germany

2. Christoph Schumann

   University of Cambridge

   Affiliation as printed

   Department of Engineering , University of Cambridge , Cambridge , CB2 1PZ , United Kingdom

3. Fabian Fröde Aachen Institute for Combustion Technology

   RWTH Aachen University

   Affiliation as printed

   Institute for Combustion Technology , RWTH Aachen University , Aachen , 52056 , Germany

4. Mohamed Gowely Aachen Institute for Combustion Technology

   RWTH Aachen University

   Affiliation as printed

   Institute for Combustion Technology , RWTH Aachen University , Aachen , 52056 , Germany

5. Temistocle Grenga

   University of Southampton

   Affiliation as printed

   Department of Aeronautics and Astronautics , Faculty of Engineering and Physical Sciences , University of Southampton , Southampton , SO17 1BJ , United Kingdom

6. Jonathan F. MacArt

   University of Notre Dame

   Affiliation as printed

   Aerospace and Mechanical Engineering , University of Notre Dame , Notre Dame , IN 46556 , USA

7. Antonio Attili

   University of Edinburgh

   Affiliation as printed

   School of Engineering , Institute for Multiscale Thermofluids , University of Edinburgh , Edinburgh , EH93FD , United Kingdom

8. Heinz G. Pitsch Aachen Institute for Combustion Technology

   RWTH Aachen University

   Affiliation as printed

   Institute for Combustion Technology , RWTH Aachen University , Aachen , 52056 , Germany