提升哈伯德模型生成效率，让大尺度低温模拟更稳定
Toward Scalable Normalizing Flows for the Hubbard Model
- 用可扩展的归一化流方法学习哈伯德模型玻尔兹曼分布
- 实现更大晶格和更低温度下的稳定采样
- 适合量子材料模拟与生成建模研究者
归一化流近期已成功学习哈伯德模型的玻尔兹曼分布，为凝聚态物理中的生成建模开辟了新路径。本文研究将此类模拟拓展至更大晶格尺寸和更低温度所需的关键步骤，重点提升稳定性与效率。同时，我们分析了随机归一化流与非平衡马尔可夫链蒙特卡洛方法在该费米子系统中的缩放行为。
原文摘要 · Abstract (English)
Normalizing flows have recently demonstrated the ability to learn the Boltzmann distribution of the Hubbard model, opening new avenues for generative modeling in condensed matter physics. In this work, we investigate the steps required to extend such simulations to larger lattice sizes and lower temperatures, with a focus on enhancing stability and efficiency. Additionally, we present the scaling behavior of stochastic normalizing flows and non-equilibrium Markov chain Monte Carlo methods for this fermionic system.
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