论文标题
用于拟合数值物理模型的优化和监督的机器学习方法
Optimization and Supervised Machine Learning Methods for Fitting Numerical Physics Models without Derivatives
论文作者
论文摘要
我们讨论了一个计算昂贵的核物理模型的校准，该模型对于该模型而言，与拟合参数相对于拟合参数的衍生信息不容易获得。特别令人感兴趣的是，当可以使用数十个而不是数百万或更多的培训数据以及模型的费用将可以执行的并发模型评估的数量限制时，基于优化的培训算法的性能。 作为案例研究，我们考虑了Fayans能量密度功能模型，该模型具有类似于核物理学中许多模型拟合和校准问题的特征。我们分析了与随机优化算法相关的高参数调整注意事项和可变性，并说明了在不同计算设置中调整的考虑因素。
We address the calibration of a computationally expensive nuclear physics model for which derivative information with respect to the fit parameters is not readily available. Of particular interest is the performance of optimization-based training algorithms when dozens, rather than millions or more, of training data are available and when the expense of the model places limitations on the number of concurrent model evaluations that can be performed. As a case study, we consider the Fayans energy density functional model, which has characteristics similar to many model fitting and calibration problems in nuclear physics. We analyze hyperparameter tuning considerations and variability associated with stochastic optimization algorithms and illustrate considerations for tuning in different computational settings.
