Paper

Diff2SP: Diffusion Models for Correlated Scenario Generation in Stochastic Programming

arXiv:2606.05649v1 Announce Type: cross Abstract: Scenario generation is a critical component in stochastic programming (SP), as it directly influences the quality of decision-making under uncertainty. Existing approaches predominantly rely on either sampling-based techniques or supervised learning using neural networks. Sampling-based techniques often struggle to capture complex dependencies and rare but plausible events, while supervised learning requires fixed input-output pairs for training and is limited in its ability to generate a wide variety of realistic scenarios that are not restri…

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