粒子蒙特卡洛方法在格点场论采样中表现优异，媲美甚至超越神经采样器。
Particle Monte Carlo methods for Lattice Field Theory
- 使用GPU加速的粒子蒙特卡洛方法，无需特定问题结构
- 在标准标量场理论测试中样本质量与耗时均优于或持平先进神经采样器
- 可同时估计配分函数，仅需单一数据驱动协方差调参
格点场论中的高维多峰采样问题已成为机器学习辅助采样方法的重要基准。我们证明，基于GPU加速的粒子方法（如顺序蒙特卡洛SMC和嵌套采样）在标准标量场理论基准上，其样本质量与实际运行时间可媲美甚至超越当前最先进的神经采样器，同时还能估计配分函数。这些方法仅需一个数据驱动的协方差进行调参，无需针对具体问题设计结构，便能实现竞争力表现，从而提升了对学习型提议方法训练成本合理性的要求。
原文摘要 · Abstract (English)
High-dimensional multimodal sampling problems from lattice field theory (LFT) have become important benchmarks for machine learning assisted sampling methods. We show that GPU-accelerated particle methods, Sequential Monte Carlo (SMC) and nested sampling, provide a strong classical baseline that matches or outperforms state-of-the-art neural samplers in sample quality and wall-clock time on standard scalar field theory benchmarks, while also estimating the partition function. Using only a single data-driven covariance for tuning, these methods achieve competitive performance without problem-specific structure, raising the bar for when learned proposals justify their training cost.
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