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Yichen Peng

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Proceedings Article | 2 May 2024 Paper

Sketch-guided flow field generation with diffusion model

Hengyuan Chang, Yichen Peng, Syuhei Sato, Haoran Xie

Proceedings Volume 13164, 131641G (2024) https://doi.org/10.1117/12.3018614

KEYWORDS: Design, Scene simulation, Fluid dynamics, Deep learning

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The convergence of flow field generation with deep learning for augmenting the efficiency of flow simulation is a prominent pursuit. Current approaches primarily utilize conditional generative adversarial networks(cGANs) to generate velocity fields guided by sketches. In contrast, the cGAN training process exhibits instability. In this study, we propose a novel 2D velocity field design and generation framework that leverages the latent diffusion model (LDM). The sketch is a constraining condition that guides the denoising process within LDM and 2D velocity field reconstruction. Our framework is proficient in generating velocity fields that align with the shape of given sketches. We verified the robustness of the proposed framework in comparison to cGAN-based methods.

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