# BaratiLab/Diffusion-based-Fluid-Super-resolution

<https://repo.masters-masters.com/en/repos/BaratiLab/Diffusion-based-Fluid-Super-resolution>

[Home](https://repo.masters-masters.com/en) › [BaratiLab](https://repo.masters-masters.com/en/owners/BaratiLab) › [Diffusion-based-Fluid-Super-resolution](https://repo.masters-masters.com/en/repos/BaratiLab/Diffusion-based-Fluid-Super-resolution)

BaratiLab/Diffusion-based-Fluid-Super-resolution has 233 stars on GitHub. Its RepoMasters card: Prototype tier. PyTorch implementation of the diffusion-based…

| Field | Value |
| --- | --- |
| Owner | [BaratiLab](https://repo.masters-masters.com/en/owners/BaratiLab) |
| Stars | 233 |
| Forks | 34 |
| Language | [Python](https://repo.masters-masters.com/en/languages/python) |
| License | MIT |
| Created | 2023 |
| Topics | computational-fluid-simulation, pytorch, super-resolution, conditional-data-generation |
| Tier | [Prototype (100 to 249 ★)](https://repo.masters-masters.com/en/ranks/rush) |
| View on GitHub | [BaratiLab/Diffusion-based-Fluid-Super-resolution](https://github.com/BaratiLab/Diffusion-based-Fluid-Super-resolution) |

## Repository

PyTorch implementation of the diffusion-based method for CFD data super-resolution proposed in the paper "A Physics-informed Diffusion Model for High-fidelity Flow Field Reconstruction".

The tier for repos with 100 to 249 ★. Published odds per card pulled: 70%. Rarity: Common.

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Data: GitHub · Illustration drawn by RepoMasters from the repo’s ID and its language color.