Effective equations in complex systems: from Langevin to machine learning

被引：5
|
作者：
Vulpiani, Angelo [1 ,2 ]
Baldovin, Marco [1 ]
机构：
[1] Univ Sapienza, Dept Phys, Roma Piazzale A Moro 5, I-00185 Rome, Italy
[2] Acad Lincei, Ctr Interdisciplinare B Segre, Via Lungara 10, I-00165 Rome, Italy
来源：
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT | 2020年 / 2020卷 / 01期
关键词：
9; 12; STATISTICAL-MECHANICS; DYNAMICS; MODEL;
D O I：
10.1088/1742-5468/ab535c
中图分类号：
O3 [力学];
学科分类号：
08 ; 0801 ;
摘要：
The problem of effective equations is reviewed and discussed. Starting from the classical Langevin equation, we show how it can be generalized to Hamiltonian systems with non-standard kinetic terms. A numerical method for inferring effective equations from data is discussed; this protocol allows to check the validity of our results. In addition we show that, with a suitable treatment of time series, such protocol can be used to infer effective models from experimental data. We briefly discuss the practical and conceptual difficulties of a pure data-driven approach in the building of models.
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收藏
页数：18
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