Optimization and supervised machine learning methods for fitting numerical physics models without derivatives*

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.

Automated summary

This paper discusses the calibration of a computationally expensive nuclear physics model, focusing on the performance of optimization-based training algorithms when training data and concurrent model evaluations are limited. Using the Fayans energy density functional model as a case study, considerations for tuning in different computational settings are analyzed and illustrated.