☆ 4.2 Article

Temperature steerable flows and Boltzmann generators

PHYSICAL REVIEW RESEARCH (2022)

相关参考文献

注意：仅列出部分参考文献，下载原文获取全部文献信息。

Article Physics, Multidisciplinary

Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models

Kim A. Nicoli et al.

Summary: In this study, we demonstrate the potential of using deep generative machine learning models for lattice field theory, which offers a promising solution to problems where MCMC methods are limited. The generative models are shown to be able to estimate the absolute value of free energy, providing a contrast to the existing MCMC-based methods which can only estimate free energy differences. Detailed numerical experiments comparing the proposed method with MCMC-based methods in two-dimensional phi(4) theory further confirm its effectiveness.

PHYSICAL REVIEW LETTERS (2021)

添加到收藏夹

Review Computer Science, Artificial Intelligence

Normalizing Flows: An Introduction and Review of Current Methods

Ivan Kobyzev et al.

Summary: Normalizing Flows are generative models that can produce tractable distributions, enabling efficient and accurate sampling and density evaluation. This survey article aims to provide a coherent and comprehensive review of the literature on the construction and use of Normalizing Flows for distribution learning, offering context and explanation of the models, reviewing current state-of-the-art literature, and identifying open questions and promising future directions.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2021)

添加到收藏夹

Article Astronomy & Astrophysics

Flow-based sampling for fermionic lattice field theories

Michael S. Albergo et al.

Summary: Algorithms based on normalizing flows are seen as promising machine learning methods for sampling complex probability distributions. Studies have demonstrated their effectiveness in scalar theories, gauge theories, and statistical systems, while developing approaches for sampling theories with dynamical fermions. These methods have been applied to the sampling of field configurations in a two-dimensional theory involving massless staggered fermions and a scalar field with Yukawa interaction.

PHYSICAL REVIEW D (2021)

添加到收藏夹

Article Astronomy & Astrophysics

Sampling using SU(N) gauge equivariant flows

Denis Boyda et al.

Summary: A flow-based sampling algorithm has been developed for SU(N) lattice gauge theories, ensuring gauge invariance by construction. This algorithm constructs flows on an SU(N) variable (or on a U(N) variable through a simple alternative) that respects matrix conjugation symmetry, and has been applied to sample distributions of single SU(N) variables and to construct flow-based samplers for SU(2) and SU(3) lattice gauge theory in two dimensions.

PHYSICAL REVIEW D (2021)

添加到收藏夹

Article Physics, Multidisciplinary

Neural Canonical Transformation with Symplectic Flows

Shuo-Hui Li et al.

PHYSICAL REVIEW X (2020)

添加到收藏夹

Article Physics, Multidisciplinary

Equivariant Flow-Based Sampling for Lattice Gauge Theory

Gurtej Kanwar et al.

PHYSICAL REVIEW LETTERS (2020)

添加到收藏夹

Article Physics, Fluids & Plasmas

Asymptotically unbiased estimation of physical observables with neural samplers

Kim A. Nicoli et al.

PHYSICAL REVIEW E (2020)

添加到收藏夹

Article Multidisciplinary Sciences

Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning

Frank Noe et al.

SCIENCE (2019)

添加到收藏夹

Article Astronomy & Astrophysics

Flow-based generative models for Markov chain Monte Carlo in lattice field theory

M. S. Albergo et al.

PHYSICAL REVIEW D (2019)

添加到收藏夹

Article Physics, Multidisciplinary

B-T phase diagram of Pd/Fe/Ir(111) computed with parallel tempering Monte Carlo

M. Boettcher et al.

NEW JOURNAL OF PHYSICS (2018)

添加到收藏夹

Article Physics, Multidisciplinary

Neural Network Renormalization Group

Shuo-Hui Li et al.

PHYSICAL REVIEW LETTERS (2018)

添加到收藏夹

Article Multidisciplinary Sciences

Temperature-dependent solvation modulates the dimensions of disordered proteins

Rene Wuttke et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2014)

添加到收藏夹

Article Mathematics, Applied

Density estimation by dual ascent of the log-likelihood

Esteban G. Tabak et al.

Communications in Mathematical Sciences (2013)

添加到收藏夹

Article Chemistry, Physical

Identification of slow molecular order parameters for Markov model construction

Guillermo Perez-Hernandez et al.

JOURNAL OF CHEMICAL PHYSICS (2013)

添加到收藏夹

Review Chemistry, Physical

Parallel tempering: Theory, applications, and new perspectives

DJ Earl et al.

PHYSICAL CHEMISTRY CHEMICAL PHYSICS (2005)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.