Reformulates CFD inference as self-supervised inpainting on tokenized velocity fields to produce reusable flow priors that handle boundary and geometry shifts better than supervised surrogates.
Conditional neural field latent diffusion model for generating spatiotemporal turbulence , volume =
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Inpainting physics: self-supervised learning for context-driven fluid simulation
Reformulates CFD inference as self-supervised inpainting on tokenized velocity fields to produce reusable flow priors that handle boundary and geometry shifts better than supervised surrogates.