arXiv (All)
open access
ZipGAN: Super-Resolution-based Generative Adversarial Network Framework for Data Compression of Direct Numerical Simulations
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 th
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Using physics-informed enhanced super-resolution generative adversarial networks for subfilter modeling in turbulent reactive flows#1328974
- AI-assisted super-resolution cosmological simulations V: Cosmology-aware super-resolution#1215798
- Few-Step Generation via Data-Space Iteration#1249526
- Dynamic mixed turbulence modeling using a super-resolution generative adversarial approach#1298028
- Generative Adversarial Networks (GANs): A Survey of Techniques and Real-World Applications#74994
Record · ID 1297981
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive