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Spectrally Decomposed Diffusion Models for Generative Turbulence Recovery
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We investigate the statistical recovery of missing physics and turbulent phenomena in fluid flows using generative machine learning. Here we develop a two-stage super-resolution method using spectral filtering to restore the high-wavenumber components of a Kolmogorov flow. We include a rigorous examination of generated samples through the lens of statistical turbulence. By extending the prior methods to a combined super-resolution and conditional high-wavenumber generation, we demonstrate turbulence recovery on a 8x upsampling task, effectively doubling the range of recovered wavenumbers.
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Cited by 1 Pith paper
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Universal Physics Simulation: A Foundational Diffusion Approach
A conditional diffusion transformer maps boundary sketches to FDTD electromagnetic field snapshots with reported test SSIM of 0.834, but the 'universal physics' and 'physics discovery' claims are not demonstrated.
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