arXiv (All)
open access
Dynamic mixed turbulence modeling using a super-resolution generative adversarial approach
A dynamic mixed super-resolution model (DMSRM) for large-eddy simulations (LESs) is proposed, which combines the traditional dynamic mixed model (DMM) formulation with the generation of super-resolved velocity fields from which the subfilter-scale (SFS) stress tensor can be computed. A data-driven s
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
- High-fidelity Three-Dimensional Flow Field Reconstruction Driven by Turbulent Fluid Dynamics#1343240
- Goal-Oriented Weighting of Reynolds-Stress Data for Learning Turbulence Models in Complex Flows#1105595
- ZipGAN: Super-Resolution-based Generative Adversarial Network Framework for Data Compression of Direct Numerical Simulations#1297981
- Shaping the Wind: Nested Potentials for Kinematically Admissible Urban Wind Prediction#1216408
Record · ID 1298028
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