début des slides

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node_modules
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let pkgs = nixpkgs.legacyPackages.${system};
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buildInputs = with pkgs; [ texlive.combined.scheme-full ];
buildInputs = with pkgs; [ texlive.combined.scheme-full nodejs ];
};
});
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---
theme: academic
class: text-white
coverAuthor: Laurent Fainsin
coverBackgroundUrl: https://git.fainsin.bzh/ENSEEIHT/projet-fin-etude-rapport/media/branch/master/assets/aube.jpg
coverBackgroundSource: Safran Media Library
coverBackgroundSourceUrl: https://medialibrary.safran-group.com/Photos/media/179440
coverDate: '2023-09-07'
themeConfig:
paginationX: r
paginationY: t
paginationPagesDisabled:
- 1
title: Projet de fin d'étude
---
<h2 class="opacity-50" style="font-size: 2rem;">Projet de Fin d'Étude</h2>
<h1 style="font-size: 2.4rem; line-height: normal;">Modèles génératifs pour la représentation latente d'aubes 3D sous forme de maillages non structurés</h1>
---
## Sommaire
<div class="h-100 flex items-center text-2xl">
- Présentation de Safran
- Modèles génératifs
- Présentation du dataset
- Génération par diffusion
- Résultats
- Conclusion
</div>
---
## Présentation rapide de Safran
---
## Modèles génératifs (traditionnels)
<img src="https://git.fainsin.bzh/ENSEEIHT/projet-fin-etude-rapport/media/branch/master/assets/online_adaptative_sampling_DOE.png" class="m-auto h-100 mt-10"/>
---
## Modèles génératifs (deep learning)
<img src="https://lilianweng.github.io/posts/2021-07-11-diffusion-models/generative-overview.png" class="m-auto h-110"/>
<a href="https://lilianweng.github.io/posts/2021-07-11-diffusion-models/" class="absolute bottom-0 font-extralight mb-1 mr-2 right-0 text-xs">Lilian Weng, 2021</a>
---
## Dataset
---
## Denoising Diffusion Probabilistic Models (DDPM)
---
## Forward process
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## Reverse process
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## Latent Diffusion Models (LDM)
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## Classifier-free Guidance (CFG)
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## Résultats
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## Vérification par Gaussian Processes (GP)
---
## Conclusion