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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A four-parameter greybody factor model reproduces the frequency-domain ringdown amplitude of comparable-mass aligned-spin mergers with mismatches of order 10^{-5}, improving existing models by two orders of magnitude.
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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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Modeling the frequency-domain ringdown amplitude of comparable-mass mergers with greybody factors
A four-parameter greybody factor model reproduces the frequency-domain ringdown amplitude of comparable-mass aligned-spin mergers with mismatches of order 10^{-5}, improving existing models by two orders of magnitude.