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Nuclear Science and Engineering Volume 186, 2017 - Issue 3
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Technical Papers

Inverse Predictive Modeling of Radiation Transport Through Optically Thick Media in the Presence of Counting Uncertainties

Dan Gabriel CacuciUniversity of South Carolina, Department of Mechanical Engineering, Columbia, South Carolina29208Correspondence[email protected]
ORCID Iconhttp://orcid.org/0000-0001-5417-5701
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Pages 199-223 | Received 30 Dec 2016, Accepted 08 Mar 2017, Published online: 08 May 2017

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Talaat Abdelhamid, Rongliang Chen & Md. Mahbub Alam. (2021) Nonlinear conjugate gradient method for identifying Young's modulus of the elasticity imaging inverse problem. Inverse Problems in Science and Engineering 29:12, pages 2165-2185.
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Dan G. Cacuci, Ruixian Fang & Madalina C. Badea. (2018) MULTI-PRED: A Software Module for Predictive Modeling of Coupled Multi-Physics Systems. Nuclear Science and Engineering 191:2, pages 187-202.
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Articles from other publishers (6)

Keith C. Bledsoe, Jason Hite, Matthew A. Jessee & Jordan P. Lefebvre. (2021) Application of Markov Chain Monte Carlo Methods for Uncertainty Quantification in Inverse Transport Problems. IEEE Transactions on Nuclear Science 68:8, pages 2210-2219.
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Dan Gabriel Cacuci. (2021) Fourth-Order Comprehensive Adjoint Sensitivity Analysis (4th-CASAM) of Response-Coupled Linear Forward/Adjoint Systems: I. Theoretical Framework. Energies 14:11, pages 3335.
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Jesús P. Curbelo & Ricardo C. Barros. (2021) A spectral nodal method for the adjoint neutral particle transport equations in -geometry: Application to direct and inverse multigroup source-detector problems . Annals of Nuclear Energy 150, pages 107822.
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Dan G. Cacuci. (2019) Reprint of “Nuclear thermal-hydraulics applications illustrating the key roles of adjoint-computed sensitivities for overcoming the curse of dimensionality in sensitivity analysis, uncertainty quantification and predictive modeling”. Nuclear Engineering and Design 354, pages 110294.
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Cacuci. (2019) Towards Overcoming the Curse of Dimensionality: The Third-Order Adjoint Method for Sensitivity Analysis of Response-Coupled Linear Forward/Adjoint Systems, with Applications to Uncertainty Quantification and Predictive Modeling. Energies 12:21, pages 4216.
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Dan G. Cacuci. (2019) Nuclear thermal-hydraulics applications illustrating the key roles of adjoint-computed sensitivities for overcoming the curse of dimensionality in sensitivity analysis, uncertainty quantification and predictive modeling. Nuclear Engineering and Design 351, pages 20-32.
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