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.

我们解决了一个计算上昂贵的核物理模型的校准问题，该模型的拟合参数派生信息不易获得。特别令人感兴趣的是，当有数十个而不是数百万个或更多的训练数据可用时，并且当模型的开销限制了可以执行的并发模型评估数量时，基于优化的训练算法的性能。作为案例研究，我们考虑了Fayans能量密度泛函模型，该模型具有与核物理中许多模型拟合和校准问题相似的特征。我们分析了与随机优化算法相关的超参数调整注意事项和可变性，并说明了在不同计算设置中进行调整的注意事项。