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2 May 2024 Sketch-guided flow field generation with diffusion model
Hengyuan Chang, Yichen Peng, Syuhei Sato, Haoran Xie
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Proceedings Volume 13164, International Workshop on Advanced Imaging Technology (IWAIT) 2024; 131641G (2024) https://doi.org/10.1117/12.3018614
Event: International Workshop on Advanced Imaging Technology (IWAIT) 2024, 2024, Langkawi, Malaysia
Abstract
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.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Hengyuan Chang, Yichen Peng, Syuhei Sato, and Haoran Xie "Sketch-guided flow field generation with diffusion model", Proc. SPIE 13164, International Workshop on Advanced Imaging Technology (IWAIT) 2024, 131641G (2 May 2024); https://doi.org/10.1117/12.3018614
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KEYWORDS
Design
Scene simulation
Deep learning
Fluid dynamics
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