Diff2SP: Diffusion Models for Correlated Scenario Generation in Stochastic Programming
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 restricted by predefined patterns or rules. To address these limitations, we introduce Diff2SP, a diffusion-based generative framework that incorporates d...
| Authors | Haixiang Sun, Andrew Liu |
|---|---|
| Primary category | stat.CO |
| Published | 4 June 2026 |
| Last updated at source | 4 June 2026 |
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