Copertina

Super-Resolution Generative Adversarial Network for Data Compression of Direct Numerical Simulations

Salvato in:
Pubblicato in:arXiv.org (Dec 18, 2024), p. n/a
Autore principale: Nista, Ludovico
Altri autori: Schumann, Christoph D K, Fröde, Fabian, Gowely, Mohamed, Grenga, Temistocle, MacArt, Jonathan F, Attili, Antonio, Pitsch, Heinz
Pubblicazione:
Cornell University Library, arXiv.org
Soggetti:
Datasets
Temporal resolution
Wavelet transforms
Velocity gradient
Data compression
Discriminators
Discrete Wavelet Transform
Generative adversarial networks
Fluid flow
Domain names
Encoding-Decoding
Direct numerical simulation
Reynolds number
Fluid dynamics
Compression ratio
Accesso online:Citation/Abstract
Full text outside of ProQuest
Tags: Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
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, cannot achieve compression ratios of 8 or higher for complex turbulent flows without introducing significant encoding/decoding errors. On the other hand, super-resolution generative adversarial networks (SR-GANs) can accurately reconstruct fine-scale features, preserving velocity gradients and structural details, even at a compression ratio of 512, thanks to the adversarial training enabled by the discriminator. Their high 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 SR-GANs can enhance dataset temporal resolution without additional simulation overhead by generating high-quality intermediate fields from compressed snapshots. The SR-GAN discriminator can reliably evaluate the quality of decoded fields, ensuring fidelity even in the absence of original DNS fields. Hence, SR-GAN-based compression/decompression methods present a highly efficient and scalable alternative for large-scale DNS storage and transfer, offering substantial advantages in terms of compression efficiency, reconstruction fidelity, and temporal resolution enhancement.
ISSN:2331-8422
Fonte:Engineering Database