A hybrid U-Net and Transformer diffusion backbone trained in residual space with a velocity parametrization outperforms baselines on 2D turbulence super-resolution and forecasting, and generalizes zero-shot to unseen Reynolds numbers and boundary conditions.
Computational design for long-term numerical integration of the equations of fluid motion: Two- dimensional incompressible flow
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FLEX: A Backbone for Diffusion-Based Modeling of Spatio-temporal Physical Systems
A hybrid U-Net and Transformer diffusion backbone trained in residual space with a velocity parametrization outperforms baselines on 2D turbulence super-resolution and forecasting, and generalizes zero-shot to unseen Reynolds numbers and boundary conditions.