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LatentDDM is identified as a promising design principle for physical foundation models that improves 20-step field rollouts in fast-pitching airfoil flow, both zero-shot and after few-shot calibration.
A factorized latent-conditioning formulation is introduced that jointly learns a neural operator and a low-dimensional latent representation through factorized prediction, trajectory-decoupled sampling, and dimension selection that enables generalization to previously unseen system instances.
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