Pere Mujal, Àlex Martínez Miguel, Artur Polls, Bruno Juliá-Díaz, Sebastiano Pilati
SciPost Phys. 10, 073 (2021) ·
published 24 March 2021
|
· pdf
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.
