Invited Talk: Generative Learning for Turbulence: Conditional Diffusion and Flow Matching Models for Spatiotemporal Flow Synthesis and Reconstruction
INVITED · P01 · ID: 3586499
Presentations
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Generative Learning for Turbulence: Conditional Diffusion and Flow Matching Models for Spatiotemporal Flow Synthesis and Reconstruction
ORAL · Invited
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Publication: 1. CoNFiLD-inlet: Synthetic Turbulence Inflow Using Generative Latent Diffusion Models with Neural Fields, Physical Review Fluids, 054901, 2025<br>2. Conditional neural field latent diffusion model for generating spatiotemporal turbulence, Nature Communications, 1, 15, 10416, 2024.<br>3. Bayesian conditional diffusion model for versatile spatiotemporal turbulence generation, Computer Methods in Applied Mechanics and Engineering, 117023, 2024<br>4. Conditional flow matching for generative modeling of near-wall turbulence with quantified uncertainty, under review in Journal of Fluid Mechanics. 2025
Presenters
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Jian-Xun Wang
Cornell University, University of Notre Dame
Authors
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Jian-Xun Wang
Cornell University, University of Notre Dame
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