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PODiff: Latent Diffusion in Proper Orthogonal Decomposition Space for Scientific Super-Resolution
arXiv CS.LG
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ArXi:2605.03399v1 Announce Type: new Probabilistic super-resolution of high-dimensional spatial fields using diffusion models is often computationally prohibitive due to the cost of operating directly in pixel space. We propose PODiff, a structured conditional generative framework that performs diffusion in a fixed, variance-ordered Proper Orthogonal Decomposition (POD) coefficient space, exploiting the orthogonality of POD modes to impose an interpretable, variance-ordered latent geometry.