Derivative-free optimization for parameter estimation in computational nuclear physics

Abstract

We consider optimization problems that arise when estimating a set of unknown parameters from experimental data, particularly in the context of nuclear density functional theory. We examine the cost of not having derivatives of these functionals with respect to the parameters. We show that the POUNDERS code for local derivative-free optimization obtains consistent solutions on a variety of computationally expensive energy density functional calibration problems. We also provide a primer on the operation of the POUNDERS software in the Toolkit for Advanced Optimization.

17 pages, 4 figures, 5 tables; Invited paper for the Journal of Physics G: Nuclear and Particle Physics focus section entitled "Enhancing the interaction between nuclear experiment and theory through information and statistics". In press

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• Error Analysis in Nuclear Density Functional Theory
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• Uncertainty Quantification and Propagation in Nuclear Density Functional Theory
• Parameter Optimisation for the Latest Quark-Meson Coupling Energy Density Functional
• Shell-model interactions from chiral effective field theory
• Superheavy Nuclei in the Quark-Meson-Coupling Model
• Optimization and Supervised Machine Learning Methods for Fitting Numerical Physics Models without Derivatives
• Optimized nuclear energy density functionals including long-range pion contributions
• Moving Beyond Chi-Squared in Nuclei and Neutron Stars
• Quantification of Uncertainties in Nuclear Density Functional theory
• Nuclear Energy Density Optimization: UNEDF2