用对称性约束的生成模型模拟费米子关联电子，突破传统方法速度与偏差瓶颈
Simulating Correlated Electrons with Symmetry-Enforced Normalizing Flows
- 设计对称性感知的归一化流架构，直接学习费米子哈伯德模型的玻尔兹曼分布
- 在时间连续极限下避免遍历性问题，计算效率比传统蒙特卡洛方法提升数倍
- 适合研究石墨烯等关联电子系统，为强关联系统数值模拟提供新范式
我们首次证明了归一化流可以准确学习费米子哈伯德模型的玻尔兹曼分布——该模型是描述石墨烯及相关材料电子结构的关键框架。现有先进方法如混合蒙特卡洛在时间连续极限附近常因遍历性问题导致估计偏差。通过引入对称性感知架构及独立同分布采样，我们的方法解决了这些问题，并在计算速度上实现显著提升。
原文摘要 · Abstract (English)
We present the first proof of principle that normalizing flows can accurately learn the Boltzmann distribution of the fermionic Hubbard model - a key framework for describing the electronic structure of graphene and related materials. State-of-the-art methods like Hybrid Monte Carlo often suffer from ergodicity issues near the time-continuum limit, leading to biased estimates. Leveraging symmetry-aware architectures as well as independent and identically distributed sampling, our approach resolves these issues and achieves significant speed-ups over traditional methods.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。