Identifying Discrete Breathers Using Convolutional Neural Networks

TL;DR

This paper demonstrates that convolutional neural networks can effectively differentiate between localized nonlinear discrete breathers and linear phonon modes in one-dimensional nonlinear chains, and also identify the underlying potentials.

Contribution

It introduces a deep learning approach to classify and analyze discrete breathers and phonons, advancing the application of AI in nonlinear lattice dynamics.

Findings

Neural networks accurately distinguish breathers from phonons.

Deep learning determines the nonlinear potentials generating breathers.

Method can be extended to complex natural systems.

Abstract

Artificial intelligence in the form of deep learning is now very popular and directly implemented in many areas of science and technology. In the present work we study time evolution of Discrete Breathers in one-dimensional nonlinear chains using the framework of Convolutional Neural Networks. We focus on differentiating discrete breathers which are localized nonlinear modes from linearized phonon modes. The breathers are localized in space and time-periodic solutions of non-linear discrete lattices while phonons are the linear collective oscillations of interacting atoms and molecules. We show that deep learning neural networks are indeed able not only to distinguish breather from phonon modes but also determine with high accuracy the underlying nonlinear on-site potentials that generate breathers. This work can have extensions to more complex natural systems.