Application of Markov Chain Monte Carlo Methods for Uncertainty Quantification in Inverse Transport Problems

Bledsoe, Keith C.; Hite, Jason; Jessee, Matthew; Lefebvre, Jordan

doi:10.1109/tns.2021.3089018

Application of Markov Chain Monte Carlo Methods for Uncertainty Quantification in Inverse Transport Problems

Journal Article · Thu Jun 17 00:00:00 EDT 2021 · IEEE Transactions on Nuclear Science

DOI:https://doi.org/10.1109/tns.2021.3089018· OSTI ID:1820875

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Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Determination of the components of a radioactive source/shield system using the system’s radiation signature is of great importance in homeland security, material safeguards, and waste management. Although significant progress has been made toward solving this inverse transport problem in recent years, work remains to be done to quantify the uncertainty in reconstructed results. In this article we apply two Markov chain Monte Carlo (MCMC) approaches, the delayed rejection adaptive metropolis (DRAM) and differential evolution adaptive metropolis (DREAM) methods, to solve inverse problems and quantify uncertainty. The DRAM method uses delayed rejection combined with global adaptation of the proposal covariance matrix. Furthermore, the DREAM method hybridizes MCMC sampling with the differential evolution (DE) algorithm. In numerical test cases, the DRAM and DREAM methods are shown to be superior to a first-order inverse Hessian approach for problems with noisy data and multiple unknown quantities, with DREAM converging to the posterior distribution more quickly than DRAM. The DREAM and DRAM results indicate that a full posterior distribution is required to quantify uncertainty in many inverse transport problems.

View Accepted Manuscript (DOE)

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA), Office of Defense Nuclear Nonproliferation

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1820875

Journal Information:: IEEE Transactions on Nuclear Science, Journal Name: IEEE Transactions on Nuclear Science Journal Issue: 8 Vol. 68; ISSN 0018-9499

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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