AL-ATCI uses active learning to identify the relevant determinant manifold in configuration-interaction impurity solvers, achieving weak scaling with bath size and reproducing exact-diagonalization accuracy for Hubbard model clusters up to size 10 and Sr2RuO4 impurities.
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Discrete harmonic morphisms ensure exact random-walk projection under network coarse-graining, and Laplacian renormalization often produces exact instances of them on real networks.
Time evolution in the time-dependent SU(2) Gross-Neveu model with RG-matched coupling is equivalent to renormalization group flow, generating an exponentially decaying dynamical mass gap in the adiabatic regime.
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A Scalable Configuration-Interaction Impurity Solver via Active Learning
AL-ATCI uses active learning to identify the relevant determinant manifold in configuration-interaction impurity solvers, achieving weak scaling with bath size and reproducing exact-diagonalization accuracy for Hubbard model clusters up to size 10 and Sr2RuO4 impurities.
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Harmonic morphisms and dynamical invariants in network renormalization
Discrete harmonic morphisms ensure exact random-walk projection under network coarse-graining, and Laplacian renormalization often produces exact instances of them on real networks.
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Time-Dependent Dynamical Dimensional Transmutation in the $SU(2)$ Gross-Neveu Model with Time-Dependent Interaction Strength
Time evolution in the time-dependent SU(2) Gross-Neveu model with RG-matched coupling is equivalent to renormalization group flow, generating an exponentially decaying dynamical mass gap in the adiabatic regime.