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Flow-based sampling in the lattice Schwinger model at criticality

Flow-based sampling in the lattice Schwinger model at criticality

M. S. Albergo
D. Boyda
Kyle Cranmer
D. Hackett
G. Kanwar
S. Racanière
Danilo Jimenez Rezende
F. Romero-López
P. Shanahan
Julian M. Urban
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Papers citing "Flow-based sampling in the lattice Schwinger model at criticality"

14 / 14 papers shown
Title
NeuMC -- a package for neural sampling for lattice field theories
Piotr Bialas
P. Korcyl
T. Stebel
Dawid Zapolski
100
0
0
14 Mar 2025
Practical applications of machine-learned flows on gauge fields
Practical applications of machine-learned flows on gauge fields
Ryan Abbott
M. S. Albergo
D. Boyda
D. Hackett
G. Kanwar
Fernando Romero-López
P. Shanahan
Julian M. Urban
AI4CE
66
11
0
17 Apr 2024
AdvNF: Reducing Mode Collapse in Conditional Normalising Flows using Adversarial Learning
AdvNF: Reducing Mode Collapse in Conditional Normalising Flows using Adversarial Learning
V. Kanaujia
Mathias S. Scheurer
Vipul Arora
GANDRL
51
2
0
29 Jan 2024
Energy based diffusion generator for efficient sampling of Boltzmann distributions
Energy based diffusion generator for efficient sampling of Boltzmann distributions
Yan Wang
Ling Guo
Hao Wu
Tao Zhou
DiffM
97
4
0
04 Jan 2024
Multi-Lattice Sampling of Quantum Field Theories via Neural Operator-based Flows
Multi-Lattice Sampling of Quantum Field Theories via Neural Operator-based Flows
Bálint Máté
Franccois Fleuret
AI4CE
119
0
0
01 Jan 2024
Diffusion Models as Stochastic Quantization in Lattice Field Theory
Diffusion Models as Stochastic Quantization in Lattice Field Theory
Lei Wang
Gert Aarts
Kai Zhou
DiffM
82
14
0
29 Sep 2023
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
AI4CE
88
26
0
03 Sep 2023
Training normalizing flows with computationally intensive target probability distributions
Training normalizing flows with computationally intensive target probability distributions
P. Białas
P. Korcyl
T. Stebel
52
5
0
25 Aug 2023
Mutual information of spin systems from autoregressive neural networks
Mutual information of spin systems from autoregressive neural networks
P. Białas
P. Korcyl
T. Stebel
36
3
0
26 Apr 2023
Learning Interpolations between Boltzmann Densities
Learning Interpolations between Boltzmann Densities
Bálint Máté
Franccois Fleuret
137
29
0
18 Jan 2023
Simulating first-order phase transition with hierarchical autoregressive networks
Simulating first-order phase transition with hierarchical autoregressive networks
P. Białas
Paul A. Czarnota
P. Korcyl
T. Stebel
44
3
0
09 Dec 2022
Aspects of scaling and scalability for flow-based sampling of lattice QCD
Aspects of scaling and scalability for flow-based sampling of lattice QCD
Ryan Abbott
M. S. Albergo
Aleksandar Botev
D. Boyda
Kyle Cranmer
...
Ali Razavi
Danilo Jimenez Rezende
F. Romero-López
P. Shanahan
Julian M. Urban
116
33
0
14 Nov 2022
Deformations of Boltzmann Distributions
Deformations of Boltzmann Distributions
Bálint Máté
Franccois Fleuret
OT
77
2
0
25 Oct 2022
Hierarchical autoregressive neural networks for statistical systems
Hierarchical autoregressive neural networks for statistical systems
P. Białas
P. Korcyl
T. Stebel
77
12
0
21 Mar 2022