Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics

3 September 2023

Papers citing "Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics"

5 / 5 papers shown

Title
Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics Gert Aarts Kenji Fukushima Tetsuo Hatsuda Andreas Ipp S. Shi Lei Wang K. Zhou AI4CE PINN 129 3 0 09 Jan 2025
On learning higher-order cumulants in diffusion models Gert Aarts Diaa E. Habibi Lei Wang K. Zhou 87 5 0 28 Oct 2024
Flow-Based Sampling for Entanglement Entropy and the Machine Learning of Defects Andrea Bulgarelli E. Cellini K. Jansen Stefan Kühn A. Nada Shinichi Nakajima K. Nicoli M. Panero 82 6 0 18 Oct 2024
Bayesian RG Flow in Neural Network Field Theories Jessica N. Howard Marc S. Klinger Anindita Maiti A. G. Stapleton 135 2 0 27 May 2024
Sampling using $SU(N)$ gauge equivariant flows D. Boyda G. Kanwar S. Racanière Danilo Jimenez Rezende M. S. Albergo Kyle Cranmer D. Hackett P. Shanahan 90 129 0 12 Aug 2020