Using machine-learning modelling to understand macroscopic dynamics in a system of coupled maps

TL;DR

This paper demonstrates how machine learning can effectively model and analyze the complex macroscopic dynamics of coupled maps, revealing insights into the underlying physics and effective system properties.

Contribution

It introduces a machine learning approach to model macroscopic dynamics and compares it with traditional methods, providing new insights into the system's physics.

Findings

ML accurately models nontrivial evolution laws

Insights into attractor dimension and memory effects

Multi-scale structure of dynamics uncovered

Abstract

Machine learning techniques not only offer efficient tools for modelling dynamical systems from data, but can also be employed as frontline investigative instruments for the underlying physics. Nontrivial information about the original dynamics, which would otherwise require sophisticated ad-hoc techniques, can be obtained by a careful usage of such methods. To illustrate this point, we consider as a case study the macroscopic motion emerging from a system of globally coupled maps. We build a coarse-grained Markov process for the macroscopic dynamics both with a machine learning approach and with a direct numerical computation of the transition probability of the coarse-grained process, and we compare the outcomes of the two analyses. Our purpose is twofold: on the one hand, we want to test the ability of the stochastic machine learning approach to describe nontrivial evolution laws, as the one considered in our study; on the other hand, we aim at gaining some insight into the physics of the macroscopic dynamics by modulating the information available to the network, we are able to infer important information about the effective dimension of the attractor, the persistence of memory effects and the multi-scale structure of the dynamics.