NucleiML is a machine learning surrogate for relativistic mean-field calculations of finite nuclei properties that accelerates Bayesian inference of nuclear equation of state parameters by roughly 1000 times.
The overall accuracy of the classifier is 92%, demonstrating its effectiveness in categorizing data, previously also observed during neural network training (Fig
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
nucl-th 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
NucleiML: A machine learning framework of ground-state properties of finite nuclei for accelerated Bayesian exploration
NucleiML is a machine learning surrogate for relativistic mean-field calculations of finite nuclei properties that accelerates Bayesian inference of nuclear equation of state parameters by roughly 1000 times.