# Super-resolution of low-fidelity flow solutions via generative adversarial networks

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/super-resolution-of-low-fidelity-flow-solutions-via-generative-adversarial/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Mahdi Pourbagian,Ali Ashrafizadeh |
| Citations | 8 |
| DOI | 10.1177/00375497211061260 |
| Fields | Computer Science,Engineering |
| Open Access | false |
| OA Status | closed |
| OpenAlex ID | https://openalex.org/W3217527491 |
| Type | article |
| Year | 2021 |

## Paper authors

- [Mahdi Pourbagian](https://scholariq.org/researchers/mahdi-pourbagian/)

## Paper primary topic

- [Advanced Image Processing Techniques](https://scholariq.org/topics/advanced-image-processing-techniques/)

## Paper topics

- [Advanced Image Processing Techniques](https://scholariq.org/topics/advanced-image-processing-techniques/)
- [Fluid Dynamics and Turbulent Flows](https://scholariq.org/topics/fluid-dynamics-and-turbulent-flows/)
- [Image and Signal Denoising Methods](https://scholariq.org/topics/image-and-signal-denoising-methods/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.