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

doi:10.1088/1361-6471/abd009

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

2020-12-31

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DOI：10.1088/1361-6471/abd009

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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.

Keywords:

model calibration

numerical optimization

density functional theory

machine learning in physics

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

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