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Title: Using a derivative-free optimization method for multiple solutions of inverse transport problems

Identifying unknown components of an object that emits radiation is an important problem for national and global security. Radiation signatures measured from an object of interest can be used to infer object parameter values that are not known. This problem is called an inverse transport problem. An inverse transport problem may have multiple solutions and the most widely used approach for its solution is an iterative optimization method. This paper proposes a stochastic derivative-free global optimization algorithm to find multiple solutions of inverse transport problems. The algorithm is an extension of a multilevel single linkage (MLSL) method where a mesh adaptive direct search (MADS) algorithm is incorporated into the local phase. Furthermore, numerical test cases using uncollided fluxes of discrete gamma-ray lines are presented to show the performance of this new algorithm.
 [1] ;  [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Report Number(s):
Journal ID: ISSN 1389-4420; PII: 9306
Grant/Contract Number:
Accepted Manuscript
Journal Name:
Optimization and Engineering
Additional Journal Information:
Journal Volume: 17; Journal Issue: 1; Journal ID: ISSN 1389-4420
Research Org:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org:
Country of Publication:
United States
42 ENGINEERING; 97 MATHEMATICS AND COMPUTING; inverse transport problem; derivative-free optimization; stochastic global optimization; multilevel single linkage; mesh adaptive direct search
OSTI Identifier: