Neural networks trained with 10-100x fewer examples than prior work approximate CT-QMC impurity solvers in DMFT, delivering comparable accuracy on interpolation and accelerating simulations up to 5x when used as initial guesses for lower temperatures.
Bl¨ umer,Mott-Hubbard Metal-Insulator Transition and Optical Conductivity in High Dimensions, Ph.D
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cond-mat.str-el 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Neural networks as low-cost surrogates for impurity solvers in quantum embedding methods
Neural networks trained with 10-100x fewer examples than prior work approximate CT-QMC impurity solvers in DMFT, delivering comparable accuracy on interpolation and accelerating simulations up to 5x when used as initial guesses for lower temperatures.