# Raman-Lab-UCLA/Multiclass\_Metasurface\_InverseDesign

<https://repo.masters-masters.com/en/repos/Raman-Lab-UCLA/Multiclass_Metasurface_InverseDesign>

[Home](https://repo.masters-masters.com/en) › [Raman-Lab-UCLA](https://repo.masters-masters.com/en/owners/Raman-Lab-UCLA) › [Multiclass\_Metasurface\_InverseDesign](https://repo.masters-masters.com/en/repos/Raman-Lab-UCLA/Multiclass_Metasurface_InverseDesign)

Raman-Lab-UCLA/Multiclass\_Metasurface\_InverseDesign has 237 stars on GitHub. Its RepoMasters card: Prototype tier. Here, we use a conditional deep…

| Field | Value |
| --- | --- |
| Owner | [Raman-Lab-UCLA](https://repo.masters-masters.com/en/owners/Raman-Lab-UCLA) |
| Stars | 237 |
| Forks | 39 |
| Language | [Python](https://repo.masters-masters.com/en/languages/python) |
| License | None |
| Created | 2021 |
| Topics | dcgan-pytorch, generative-adversarial-network, nanophotonics |
| Tier | [Prototype (100 to 249 ★)](https://repo.masters-masters.com/en/ranks/rush) |
| View on GitHub | [Raman-Lab-UCLA/Multiclass\_Metasurface\_InverseDesign](https://github.com/Raman-Lab-UCLA/Multiclass_Metasurface_InverseDesign) |

## Repository

Here, we use a conditional deep convolutional generative adversarial network (cDCGAN) to inverse design across multiple classes of metasurfaces. Reference: https://onlinelibrary.wiley.com/doi/10.1002/adom.202100548

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.