论文标题
与内核脊回归有关核质量和分离能的多任务学习
Multi-task learning on nuclear masses and separation energies with the kernel ridge regression
论文作者
论文摘要
通过将梯度内核函数引入内核脊回归（KRR）方法，开发了一个称为梯度内核脊回归的多任务学习（MTL）框架，称为梯度内核脊回归。通过以WS4质量模型为例，梯度KRR网络接受了质量模型残差的训练，即质量和理论值之间的偏差和一单核子分离能之间的偏差，以提高理论预测的准确性。通过梯度KRR方法在核质量和分离能的插值和外推预测中都可以实现显着改善。这证明了当前的MTL框架的优势，该框架整合了核质量和分离能的信息，并改善了它们的预测。
A multi-task learning (MTL) framework, called gradient kernel ridge regression, for nuclear masses and separation energies is developed by introducing gradient kernel functions to the kernel ridge regression (KRR) approach. By taking the WS4 mass model as an example, the gradient KRR network is trained with the mass model residuals, i.e., deviations between experimental and theoretical values of masses and one-nucleon separation energies, to improve the accuracy of theoretical predictions. Significant improvements are achieved by the gradient KRR approach in both the interpolation and the extrapolation predictions of nuclear masses and separation energies. This demonstrates the advantage of the present MTL framework that integrates the information of nuclear masses and separation energies and improves the predictions for both of them.
