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

Supervised learning of few dirty bosons with variable particle number