2025/02/05 by Kieran Holland, Holland, Kieran, Andreas Ipp +5
Engineering · Mathematics · Physics and Astronomy · #FOS: Physical sciences #High Energy Physics - Lattice (hep-lat) #Mathematical Biology Tumor Growth #Model Reduction and Neural Networks #Reservoir Engineering and Simulation Methods
paper · pdf · doi:10.48550/arxiv.2502.03315
openalex publication_date 2025/02/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Fixed-point (FP) lattice actions are classically perfect, i.e., they have continuum classical properties unaffected by discretization effects and are expected to have suppressed lattice artifacts at weak coupling. Therefore they provide a possible way to extract continuum physics with coarser lattices, allowing to circumvent problems with critical slowing down and topological freezing towards the continuum limit. We use machine-learning methods to parameterize a FP action for four-dimensional SU(3) gauge theory using lattice gauge-covariant convolutional neural networks. The large operator space allows us to find superior parameterizations compared to previous studies and we show how such actions can be efficiently simulated with the Hybrid Monte Carlo algorithm. Furthermore, we argue that FP lattice actions can be used to define a classically perfect gradient flow without any lattice artifacts at tree level. We present initial results for scaling of the gradient flow with the FP action.