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Restricted Boltzmann Machines Propagators for Auxiliary Field Diffusion Monte Carlo

Autor: Fox, Jordan M. R., Lovato, Alessandro, Roggero, Alessandro, Rrapaj, Ermal

The auxiliary field diffusion Monte Carlo method uses imaginary-time projection techniques to accurately solve the ground-state wave function of atomic nuclei and infinite nuclear matter. In this work, we present a novel representation of the imagina

Externí odkaz: http://arxiv.org/abs/2407.14632

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Illuminating Systematic Trends in Nuclear Data with Generative Machine Learning Models

Autor: Fox, Jordan M. R., Wendt, Kyle A.

We introduce a novel method for studying systematic trends in nuclear reaction data using generative adversarial networks. Libraries of nuclear cross section evaluations exhibit intricate systematic trends across the nuclear landscape, and predictive

Externí odkaz: http://arxiv.org/abs/2403.16389

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Uncertainty quantification of transition operators in the empirical shell model

Autor: Fox, Jordan M. R., Johnson, Calvin W., Perez, Rodrigo Navarro

While empirical shell model calculations have successfully described low-lying nuclear data for decades, only recently has significant effort been made to quantify the uncertainty in such calculations. Here we quantify the statistical error in effect

Externí odkaz: http://arxiv.org/abs/2206.14956

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Uncertainty quantification of an empirical shell-model interaction using principal component analysis

Autor: Fox, Jordan M. R., Johnson, Calvin W., Perez, Rodrigo Navarro

Publikováno v: Phys. Rev. C 101, 054308 (2020)

Recent investigations have emphasized the importance of uncertainty quantification (UQ) to describe errors in nuclear theory. We carry out UQ for configuration-interaction shell model calculations in the $1s$-$0d$ valence space, investigating the sen

Externí odkaz: http://arxiv.org/abs/1911.05208

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