AI RESEARCH
On the Regularity and Generalization of One-Step Wasserstein-guided Generative Models for PDE-Induced Measures
arXiv CS.LG
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ArXi:2605.21388v1 Announce Type: new Despite the remarkable empirical success of generative models, the available theory on their statistical accuracy in scientific computing remains largely pessimistic. This paper develops a theoretical framework for understanding the regularity of transport maps and the generalization properties of one-step Wasserstein-guided generative models for PDE-induced probability measures.