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

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

Kyle Cranmer
G. Kanwar
S. Racanière
Danilo Jimenez Rezende
P. Shanahan
ArXiv (abs)PDFHTML

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
Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics
Gert Aarts
Kenji Fukushima
Tetsuo Hatsuda
Andreas Ipp
S. Shi
Lei Wang
K. Zhou
AI4CEPINN
129
3
0
09 Jan 2025
On learning higher-order cumulants in diffusion models
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
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
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
Sampling using SU(N)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

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