Derivative-free optimization for parameter estimation in computational nuclear physics

doi:10.1088/0954-3899/42/3/034031

2015-02-05

Stefan M. Wild *

J. Sarich

N. Schunck

DOI：10.1088/0954-3899/42/3/034031

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摘要

En 中文

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.

Keyword:

numerical optimization

parameter estimation

density functional theory

sensitivity analysis

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期刊

Journal of Physics G: Nuclear and Particle Physics

IF:

3.5

论文数:

6.8K

被引数:

7.7K

机构

Argonne National Laboratory

学者数:

1.1W

论文数: 9.2K

被引数: 3.8W

united states department of energy (doe)

学者数:

11.3W

论文数: 9.6W

被引数: 246

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