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Fix typo: GSoc → GSoC in Yurii Halychanskyi entry #215

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Fix typo: GSoc → GSoC in Yurii Halychanskyi entry #215

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4 changes: 2 additions & 2 deletions README.md
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Expand Up @@ -102,7 +102,7 @@ Their work has been published as a [paper](https://iopscience.iop.org/article/10
### 3.2 Dark matter property estimation through regression

Another means of dark matter study through strong lensing is through the approximation of their properties. **Yurii Halychanskyi** and **Zhongchao Guan** approximate the mass density of vortex substructure of dark matter condensates on the three datasets, Model 1, 2 and 3.
Yurii uses the ResNet18Hybrid and CmtTi architectures in their [GSoc 2021](https://summerofcode.withgoogle.com/archive/2021/projects/5719965138681856) and [2022](https://summerofcode.withgoogle.com/archive/2022/projects/58Y5QOU4) projects, while Zhongchao demonstres with ResNet18, ViT, CNN-T, MobileNet V2 and CvT-13, in their [GSoc 2022 project](https://summerofcode.withgoogle.com/archive/2022/projects/lnptRFqq).
Yurii uses the ResNet18Hybrid and CmtTi architectures in their [GSoC 2021](https://summerofcode.withgoogle.com/archive/2021/projects/5719965138681856) and [2022](https://summerofcode.withgoogle.com/archive/2022/projects/58Y5QOU4) projects, while Zhongchao demonstres with ResNet18, ViT, CNN-T, MobileNet V2 and CvT-13, in their [GSoC 2022 project](https://summerofcode.withgoogle.com/archive/2022/projects/lnptRFqq).

### 3.3 Super-resolution of lensing images

Expand All @@ -115,4 +115,4 @@ Finally, DeepLense help combat the problem of noisy and low-resolution of real l
**Pranath Reddy** performs a comparative study of the super-resolution of strong lensing images in their [GSoC 2023 project](https://summerofcode.withgoogle.com/archive/2023/projects/Rh8kJLr4), using Residual Models with Content Loss and Conditional Diffusion Models, on the Model 1 dataset.

#### 3.3.3 Physics-Informed Unsupervised Super-Resolution of Strong Lensing Images
**Anirudh Shankar** explores the unsupervised super-resolution of strong lensing images through a Physics-Informed approach in his [GSoC 2024 project](https://summerofcode.withgoogle.com/programs/2024/projects/AvlaMMJJ), built to handle sparse datasets. They use custom datasets using different lens models and light profiles.
**Anirudh Shankar** explores the unsupervised super-resolution of strong lensing images through a Physics-Informed approach in his [GSoC 2024 project](https://summerofcode.withgoogle.com/programs/2024/projects/AvlaMMJJ), built to handle sparse datasets. They use custom datasets using different lens models and light profiles.