Flow-based sampling for fermionic lattice field theories

10 June 2021

M. S. Albergo

G. Kanwar

S. Racanière

Danilo Jimenez Rezende

Papers citing "Flow-based sampling for fermionic lattice field theories"

22 / 22 papers shown

Title
NeuMC -- a package for neural sampling for lattice field theories Piotr Bialas P. Korcyl T. Stebel Dawid Zapolski 39 0 0 14 Mar 2025
Markovian Flow Matching: Accelerating MCMC with Continuous Normalizing Flows A. Cabezas Louis Sharrock Christopher Nemeth 34 1 0 23 May 2024
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 36 11 0 17 Apr 2024
AdvNF: Reducing Mode Collapse in Conditional Normalising Flows using Adversarial Learning V. Kanaujia Mathias S. Scheurer Vipul Arora GAN DRL 22 2 0 29 Jan 2024
Multi-Lattice Sampling of Quantum Field Theories via Neural Operator-based Flows Bálint Máté Franccois Fleuret AI4CE 34 0 0 01 Jan 2024
Diffusion Models as Stochastic Quantization in Lattice Field Theory Lei Wang Gert Aarts Kai Zhou DiffM 32 14 0 29 Sep 2023
Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics Kyle Cranmer G. Kanwar S. Racanière Danilo Jimenez Rezende P. Shanahan AI4CE 29 27 0 03 Sep 2023
Training normalizing flows with computationally intensive target probability distributions P. Białas P. Korcyl T. Stebel 18 5 0 25 Aug 2023
Sampling the lattice Nambu-Goto string using Continuous Normalizing Flows M. Caselle E. Cellini A. Nada 25 14 0 03 Jul 2023
Understanding Deep Generative Models with Generalized Empirical Likelihoods Suman V. Ravuri Mélanie Rey S. Mohamed M. Deisenroth VLM 27 5 0 16 Jun 2023
Geometrical aspects of lattice gauge equivariant convolutional neural networks J. Aronsson David I. Müller Daniel Schuh 31 7 0 20 Mar 2023
Learning Interpolations between Boltzmann Densities Bálint Máté Franccois Fleuret 29 23 0 18 Jan 2023
Simulating first-order phase transition with hierarchical autoregressive networks P. Białas Paul A. Czarnota P. Korcyl T. Stebel 9 3 0 09 Dec 2022
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 32 33 0 14 Nov 2022
Deformations of Boltzmann Distributions Bálint Máté Franccois Fleuret OT 28 2 0 25 Oct 2022
Applications of Machine Learning to Lattice Quantum Field Theory D. Boyda Salvatore Cali Sam Foreman L. Funcke D. Hackett ... Gert Aarts A. Alexandru Xiao-Yong Jin B. Lucini P. Shanahan AI4CE 29 19 0 10 Feb 2022
Estimating the Euclidean quantum propagator with deep generative modeling of Feynman paths Yanming Che C. Gneiting Franco Nori 35 6 0 06 Feb 2022
Stochastic normalizing flows as non-equilibrium transformations M. Caselle E. Cellini A. Nada M. Panero 36 34 0 21 Jan 2022
Machine Learning Trivializing Maps: A First Step Towards Understanding How Flow-Based Samplers Scale Up L. Debbio Joe Marsh Rossney Michael Wilson 16 6 0 31 Dec 2021
Machine Learning in Nuclear Physics A. Boehnlein M. Diefenthaler C. Fanelli M. Hjorth-Jensen T. Horn ... M. Schram A. Scheinker Michael S. Smith Xin-Nian Wang Veronique Ziegler AI4CE 37 41 0 04 Dec 2021
Scaling Up Machine Learning For Quantum Field Theory with Equivariant Continuous Flows P. D. Haan Corrado Rainone Miranda C. N. Cheng Roberto Bondesan AI4CE 13 35 0 06 Oct 2021
Orbital MCMC Kirill Neklyudov Max Welling 26 7 0 15 Oct 2020