13 citations · 14 across the 6 of their papers we have counts for
8 papers
Neural Field Transformations for Hybrid Monte Carlo: Architectural Design and Scaling
Jinchen He, Xiao-Yong Jin, James C. Osborn +1
Critical slowing down, where autocorrelation grows rapidly near the continuum limit due to Hybrid Monte Carlo (HMC) moving through configuration space inefficiently, still challeng…
Aurora: Architecting Argonne's First Exascale Supercomputer for Accelerated Scientific Discovery
William E. Allcock, Benjamin S. Allen, James Anchell +106
Aurora is Argonne National Laboratory's pioneering Exascale supercomputer, designed to accelerate scientific discovery with cutting-edge architectural innovations. Key new technolo…
Neural Network Gauge Field Transformation for 4D SU(3) gauge fields
Xiao-Yong Jin
We construct neural networks that work for any Lie group and maintain gauge covariance, enabling smooth, invertible gauge field transformations. We implement these transformations…
MLMC: Machine Learning Monte Carlo for Lattice Gauge Theory
Sam Foreman, Xiao-Yong Jin, James C. Osborn
We present a trainable framework for efficiently generating gauge configurations, and discuss ongoing work in this direction. In particular, we consider the problem of sampling con…
Applications of Machine Learning to Lattice Quantum Field Theory
Denis Boyda, Salvatore Calì, Sam Foreman +8
There is great potential to apply machine learning in the area of numerical lattice quantum field theory, but full exploitation of that potential will require new strategies. In th…
LeapfrogLayers: A Trainable Framework for Effective Topological Sampling
Sam Foreman, Xiao-Yong Jin, James C. Osborn
We introduce LeapfrogLayers, an invertible neural network architecture that can be trained to efficiently sample the topology of a 2D lattice gauge theory. We show an improv…