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We investigate the applicability of machine learning techniques in studyingthe finite-size effects associated with many-body physics. These techniqueshave an emerging presence in many-body theory as they have been used forinterpolations, extrapolations, and in modeling wavefunctions. We will resolveseveral issues associated with machine learning and many-body calculations suchas small datasets, outliers, and discontinuities, for the purpose ofextrapolating finite calculations to macroscopic scales. We carry out asystematic investigation of two related systems by developing metrics that aimto avoid spurious effects and capture desired features. This work uses neuralnetworks to extrapolate the Unitary Gas to the thermodynamic limit atzero-range, which is otherwise difficult to reach. The effective mass ofstrongly interacting neutron matter is also studied and makes use of thenon-interacting problem to resolve discontinuous predictions. For thisinvestigation, we also carried out new Auxiliary Field Diffusion Monte Carlo(AFDMC) calculations for a variety of densities and particle numbers.Ultimately, we demonstrate an effective utility for neural networks in thiscontext.

Machine-learning approach to finite-size effects in systems with strongly interacting fermions