2025/02/04 by Scott Lawrence, Lawrence, Scott
Computer Science · Decision Sciences · Materials Science · #Distributed and Parallel Computing Systems #FOS: Physical sciences #High Energy Physics - Lattice (hep-lat) #Machine Learning in Materials Science #Scientific Computing and Data Management
paper · pdf · doi:10.48550/arxiv.2502.02670
openalex publication_date 2025/02/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The last decade has seen an explosive growth of interest in exploiting developments in machine learning to accelerate lattice QCD calculations. On the sampling side, generative models are a promising approach to mitigating critical slowing down and topological freezing. Meanwhile, signal-to-noise problems have been shown to be improvable by the use of optimized improved observables. Both techniques can be made free of bias, resulting in trustworthy but reduced statistical errors. This talk reviews recent developments in this field.