Neural Network Renormalization Group

8 February 2018

Lei Wang

Papers citing "Neural Network Renormalization Group"

23 / 23 papers shown

Title
Multilevel Generative Samplers for Investigating Critical Phenomena Ankur Singha E. Cellini K. Nicoli K. Jansen Stefan Kühn Shinichi Nakajima 56 1 0 11 Mar 2025
Accurate and thermodynamically consistent hydrogen equation of state for planetary modeling with flow matching Hao Xie Saburo Howard Guglielmo Mazzola 36 1 0 17 Jan 2025
Generating configurations of increasing lattice size with machine learning and the inverse renormalization group Dimitrios Bachtis AI4CE 23 1 0 25 May 2024
Deep generative modelling of canonical ensemble with differentiable thermal properties Shuo-Hui Li Yao-Wen Zhang Ding Pan DRL SyDa 31 1 0 29 Apr 2024
Iterated Denoising Energy Matching for Sampling from Boltzmann Densities Tara Akhound-Sadegh Jarrid Rector-Brooks A. Bose Sarthak Mittal Pablo Lemos ... Siamak Ravanbakhsh Gauthier Gidel Yoshua Bengio Nikolay Malkin Alexander Tong DiffM 34 41 0 09 Feb 2024
Statistical Guarantees of Group-Invariant GANs Ziyu Chen M. Katsoulakis Luc Rey-Bellet Wei-wei Zhu 34 2 0 22 May 2023
Compressing neural network by tensor network with exponentially fewer variational parameters Yong Qing Ke Li P. Zhou Shi-Ju Ran 14 6 0 10 May 2023
Neural Diffeomorphic Non-uniform B-spline Flows S. Hong S. Chun 30 1 0 07 Apr 2023
Rigid Body Flows for Sampling Molecular Crystal Structures Jonas Köhler Michele Invernizzi P. D. Haan Frank Noé AI4CE 32 27 0 26 Jan 2023
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 24 33 0 14 Nov 2022
Blind Super-Resolution for Remote Sensing Images via Conditional Stochastic Normalizing Flows Hanlin Wu Ning Ni Shan Wang Li-bao Zhang 30 8 0 14 Oct 2022
Deep Variational Free Energy Approach to Dense Hydrogen H.-j. Xie Ziqun Li Han Wang Linfeng Zhang Lei Wang 32 9 0 13 Sep 2022
Nonperturbative renormalization for the neural network-QFT correspondence Harold Erbin Vincent Lahoche D. O. Samary 11 30 0 03 Aug 2021
Adding machine learning within Hamiltonians: Renormalization group transformations, symmetry breaking and restoration Dimitrios Bachtis Gert Aarts B. Lucini AI4CE 15 19 0 30 Sep 2020
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 20 127 0 12 Aug 2020
Learning entropy production via neural networks Dong-Kyum Kim Youngkyoung Bae Sangyun Lee Hawoong Jeong 6 38 0 09 Mar 2020
Stochastic Normalizing Flows Hao Wu Jonas Köhler Frank Noé 37 176 0 16 Feb 2020
Learning the Ising Model with Generative Neural Networks Francesco DÁngelo Lucas Böttcher AI4CE 8 28 0 15 Jan 2020
A Probability Density Theory for Spin-Glass Systems Gavin Hartnett Masoud Mohseni 11 3 0 03 Jan 2020
Flow-based generative models for Markov chain Monte Carlo in lattice field theory M. S. Albergo G. Kanwar P. Shanahan AI4CE 12 216 0 26 Apr 2019
Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data Yinhao Zhu N. Zabaras P. Koutsourelakis P. Perdikaris PINN AI4CE 28 854 0 18 Jan 2019
Pixel Recurrent Neural Networks Aaron van den Oord Nal Kalchbrenner Koray Kavukcuoglu SSeg GAN 230 2,545 0 25 Jan 2016
MCMC using Hamiltonian dynamics Radford M. Neal 179 3,262 0 09 Jun 2012