用扩散模型学习带符号问题的复朗之万过程生成分布

Diffusion models learn distributions generated by complex Langevin dynamics

• 用扩散模型从复朗之万过程数据中学习概率分布
• 首次验证扩散模型可捕捉复杂朗之万过程的真实分布
• 适合量子场论与统计物理中分布建模的研究者

具有符号问题的理论所对应的有效概率分布，其由复朗之万过程采样时难以事先确定且长期难以理解。扩散模型作为一类生成式人工智能，能够从数据中学习分布。本文探索了扩散模型学习复朗之万过程生成分布的能力，展示了其在复杂系统分布建模中的潜力。

原文摘要 · Abstract (English)

The probability distribution effectively sampled by a complex Langevin process for theories with a sign problem is not known a priori and notoriously hard to understand. Diffusion models, a class of generative AI, can learn distributions from data. In this contribution, we explore the ability of diffusion models to learn the distributions created by a complex Langevin process.

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