用神经网络模拟核力，能跨同位素链预测能量与半径。
Global Framework for Emulation of Nuclear Calculations
- 结合从头算与贝叶斯神经网络构建分层模拟器。
- 在氧同位素链上准确预测基态能量和电荷半径。
- 可全局分析核结合能对低能常数的敏感性。
我们提出一种分层框架，将从头算多体计算与贝叶斯神经网络结合，构建出可同时准确预测不同同位素链核性质的模拟器，并适用于核素图的不同区域。我们在氧同位素链上进行基准测试，实现了对基态能量和核电荷半径的精确预测，同时提供可靠的不确定性量化。该框架支持对核结合能和电荷半径相对于描述核力的低能常数的全局敏感性分析。
原文摘要 · Abstract (English)
We introduce a hierarchical framework that combines ab initio many-body calculations with a Bayesian neural network, developing emulators capable of accurately predicting nuclear properties across isotopic chains simultaneously and being applicable to different regions of the nuclear chart. We benchmark our developments using the oxygen isotopic chain, achieving accurate results for ground-state energies and nuclear charge radii, while providing robust uncertainty quantification. Our framework enables global sensitivity analysis of nuclear binding energies and charge radii with respect to the low-energy constants that describe the nuclear force.
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