GenCFD Diffusion Models Capture Turbulent Flow Statistics Missed by Deterministic Surrogates
bravo_abad · x · 2026-08-17
Bogdan Raonić et al. introduce GenCFD, a generative AI framework using conditional score-based diffusion models to learn distributions for 3D turbulent flows directly.
- Core Problem: Standard neural PDE surrogates trained with mean-squared error predict single trajectories. In chaotic systems, this drives models toward the conditional mean, causing ensemble collapse, vanishing variance, and smoothed-out fine turbulent structures.
- Solution: GenCFD generates multiple physically plausible future trajectories given initial or boundary conditions, capturing critical turbulent statistics that deterministic surrogates miss.
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