Supervised learning of few dirty bosons with variable particle number

TL;DR

This paper demonstrates a neural network approach to predict properties of few interacting bosons in disordered systems, showing universal scaling and enabling extrapolation to larger system sizes through transfer learning.

Contribution

It introduces a neural network architecture capable of handling heterogeneous datasets and employs transfer learning to efficiently predict larger system behaviors.

Findings

Universal power-law scaling in learning curves across particle numbers and interactions

Accurate predictions for multiple system sizes within training data

Transfer learning significantly accelerates learning for larger systems

Abstract

We investigate the supervised machine learning of few interacting bosons in optical speckle disorder via artificial neural networks. The learning curve shows an approximately universal power-law scaling for different particle numbers and for different interaction strengths. We introduce a network architecture that can be trained and tested on heterogeneous datasets including different particle numbers. This network provides accurate predictions for all system sizes included in the training set and, by design, is suitable to attempt extrapolations to (computationally challenging) larger sizes. Notably, a novel transfer-learning strategy is implemented, whereby the learning of the larger systems is substantially accelerated and made consistently accurate by including in the training set many small-size instances.