Complex optimization for big computational and experimental neutron datasets.

Published

Journal Article

We present a framework to use high performance computing to determine accurate solutions to the inverse optimization problem of big experimental data against computational models. We demonstrate how image processing, mathematical regularization, and hierarchical modeling can be used to solve complex optimization problems on big data. We also demonstrate how both model and data information can be used to further increase solution accuracy of optimization by providing confidence regions for the processing and regularization algorithms. We use the framework in conjunction with the software package SIMPHONIES to analyze results from neutron scattering experiments on silicon single crystals, and refine first principles calculations to better describe the experimental data.

Full Text

Duke Authors

Cited Authors

  • Bao, F; Archibald, R; Niedziela, J; Bansal, D; Delaire, O

Published Date

  • December 2016

Published In

Volume / Issue

  • 27 / 48

Start / End Page

  • 484002 -

PubMed ID

  • 27819795

Pubmed Central ID

  • 27819795

Electronic International Standard Serial Number (EISSN)

  • 1361-6528

International Standard Serial Number (ISSN)

  • 0957-4484

Digital Object Identifier (DOI)

  • 10.1088/0957-4484/27/48/484002

Language

  • eng