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

Bollapragada R, Menickelly M, Nazarewicz W, O'Neal J, Reinhard PG, Wild SM (2021)

Publication Type: Journal article

Publication year: 2021

Journal

Journal of Physics G: Nuclear and Particle Physics IOP Publishing: arXiv

Book Volume: 48

Journal Issue: 2

DOI: 10.1088/1361-6471/abd009

Abstract

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.

Authors with CRIS profile

Paul-Gerhard Reinhard Naturwissenschaftliche Fakultät

Involved external institutions

Argonne National Laboratory US United States (USA) (US) Michigan State University US United States (USA) (US)

How to cite

APA:

Bollapragada, R., Menickelly, M., Nazarewicz, W., O'Neal, J., Reinhard, P.-G., & Wild, S.M. (2021). Optimization and supervised machine learning methods for fitting numerical physics models without derivatives*. Journal of Physics G: Nuclear and Particle Physics, 48(2). https://doi.org/10.1088/1361-6471/abd009

MLA:

Bollapragada, Raghu, et al. "Optimization and supervised machine learning methods for fitting numerical physics models without derivatives*." Journal of Physics G: Nuclear and Particle Physics 48.2 (2021).

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