Adding machine learning within Hamiltonians: Renormalization group
transformations, symmetry breaking and restoration

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Adding machine learning within Hamiltonians: Renormalization group transformations, symmetry breaking and restoration

30 September 2020

Dimitrios Bachtis

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Papers citing "Adding machine learning within Hamiltonians: Renormalization group transformations, symmetry breaking and restoration"

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Title
A simple framework for contrastive learning phases of matterChinese Physics Letters (CPL), 2022 Xiaodong Han Sheng-Song Xu Zhen Feng Rong-Qiang He Zhong-Yi Lu 96 7 0 11 May 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 196 24 0 10 Feb 2022
Feature extraction of machine learning and phase transition point of Ising model S. Funai 130 3 0 22 Nov 2021
Quantum field theories, Markov random fields and machine learning Dimitrios Bachtis Gert Aarts B. Lucini AI4CE 110 8 0 21 Oct 2021
Quantitative analysis of phase transitions in two-dimensional XY models using persistent homologyPhysical Review E (PRE), 2021 Nicholas Sale Jeffrey Giansiracusa B. Lucini 151 19 0 22 Sep 2021
Machine learning with quantum field theories Dimitrios Bachtis Gert Aarts B. Lucini AI4CE 87 2 0 16 Sep 2021
Quantum field-theoretic machine learning Dimitrios Bachtis Gert Aarts B. Lucini AI4CE 156 32 0 18 Feb 2021