Particle Monte Carlo methods for Lattice Field Theory
Abstract
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.
Cite
@article{arxiv.2511.15196,
title = {Particle Monte Carlo methods for Lattice Field Theory},
author = {David Yallup},
journal= {arXiv preprint arXiv:2511.15196},
year = {2025}
}
Comments
To appear in the NeurIPS 2025 workshop, Frontiers in Probabilistic Inference: Sampling Meets Learning