Effective Equations in complex systems: from Langevin to machine learning
Statistical Mechanics
2020-01-29 v1
Abstract
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.
Cite
@article{arxiv.1911.08419,
title = {Effective Equations in complex systems: from Langevin to machine learning},
author = {Angelo Vulpiani and Marco Baldovin},
journal= {arXiv preprint arXiv:1911.08419},
year = {2020}
}