ノガワ　トモアキ   Nogawa Tomoaki   能川　知昭    所属   東邦大学  医学部 医学科    職種   講師
論文種別	原著
言語種別	英語
査読の有無	査読あり
表題	Dimensional Reduction of Dynamical Systems by Machine Learning: Automatic Generation of the Optimum Extensive Variables and Their Time-Evolution Map
掲載誌名	正式名：Journal of Statistical Mechanics: Theory and Experiment 略　 称：J. Stat. Mech.
掲載区分	国外
巻・号・頁	pp.123404
著者・共著者	Tomoaki Nogawa
担当区分	筆頭著者
発行年月	2023/12/18
概要	A framework is proposed to generate a phenomenological model that extracts the essence of a dynamical system (DS) with large degrees of freedom using machine learning. For a given microscopic DS, the optimum transformation to a small number of macroscopic variables, which is expected to be extensive, and the rule of time evolution that the variables obey are simultaneously identified. The utility of this method is demonstrated through its application to the nonequilibrium relaxation of the three-state Potts model.