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This content will become publicly available on November 7, 2017

Title: Complex optimization for big computational and experimental neutron datasets

Here, 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. Finally, 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.
Authors:
 [1] ;  [2] ;  [3] ;  [4] ;  [4] ;  [5]
  1. Univ. of Tennessee, Chattanooga, TN (United States). Dept. of Mathematics
  2. (ORNL), Oak Ridge, TN (United States). Computer Science and Mathematics Division
  3. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Computer Science and Mathematics Division
  4. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Materials Science & Technology Division
  5. Duke Univ., Durham, NC (United States). Dept. of Mechanical Engineering and Materials Science
Publication Date:
OSTI Identifier:
1354653
Grant/Contract Number:
AC05-00OR22725; SC0016166; SC0001299
Type:
Accepted Manuscript
Journal Name:
Nanotechnology
Additional Journal Information:
Journal Volume: 27; Journal Issue: 48; Journal ID: ISSN 0957-4484
Publisher:
IOP Publishing
Research Org:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Spallation Neutron Source (SNS)
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 36 MATERIALS SCIENCE; image processing; inelastic neutron scattering; hierarchical optimization; mathematical regularization