RSOM applies dictionary learning to discover a sparse dictionary that conditions the analytic continuation inverse problem, yielding competitive results on synthetic tests and finite-temperature electron gas QMC data.
Bryan, Maximum entropy analysis of oversampled data problems, European Biophysics Journal 18 (1990) 165–174
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Sign-free DQMC on three-valley Hubbard models at three electrons per cell maps an extended intermediate-coupling regime with competing local-moment formation and itinerancy, plus U(6) crossovers to ordered states, for near-isotropic interactions relevant to materials like twisted SnSe2.
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Discovering a well-conditioned analytic continuation problem via dictionary learning
RSOM applies dictionary learning to discover a sparse dictionary that conditions the analytic continuation inverse problem, yielding competitive results on synthetic tests and finite-temperature electron gas QMC data.