2026-04-13T04:08:44Zhttps://recercat.cat/oai/request
oai:recercat.cat:2445/1954552025-12-05T16:48:46Zcom_2072_1057col_2072_478823col_2072_478904col_2072_478917
RECERCAT author Mujal Torreblanca, Pere author Martínez Miguel, Alex author Polls Martí, Artur author Juliá-Díaz, Bruno author Pilati, Sebastiano 2023-03-17T15:52:01Z2023-03-17T15:52:01Z2021-03-242023-03-17T15:52:01Z 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. cc-by (c) Mujal Torreblanca, Pere et al., 2021 https://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Bosons Física de partícules Teoria quàntica Bosons Particle physics Quantum theory Supervised learning of few dirty bosons with variable particle number info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion