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Journal of Computational and Theoretical Transport Volume 45, 2016 - Issue 7: Special Issue Part 5 of 5: Papers from the 24th International Conference on Transport Theory
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Original Articles

Application of Variational Variance Reduction for Source-Detector Problems in Nuclear Non-proliferation

E. SomasundaramSchool of Nuclear Science and Engineering, Oregon State University, Corvallis, OR, USACorrespondence[email protected]
&
T. PalmerSchool of Nuclear Science and Engineering, Oregon State University, Corvallis, OR, USA
Pages 554-557 | Published online: 14 Oct 2016

Citations (5)

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Read on this site (2)

C. B. Pazinatto, L. B. Barichello & H. R. B. Orlande. (2021) A Bayesian approach for neutral particles source estimation. Inverse Problems in Science and Engineering 29:1, pages 95-130.
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C. B. Pazinatto & L. B. Barichello. (2018) Energy Dependent Source Reconstructions via Explicit Formulations of the Adjoint Particles Flux. Journal of Computational and Theoretical Transport 47:1-3, pages 58-83.
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Articles from other publishers (3)

Qingquan Pan, Huanwen Lv, Songqian Tang, Jinbiao Xiong & Xiaojing Liu. (2023) Pointing probability Driven semi-analytic Monte Carlo Method (PDMC) – Part I: Global variance reduction for large-scale radiation transport analysis. Computer Physics Communications 291, pages 108850.
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Qingquan Pan, Lianjie Wang, Yun Cai, Xiaojing Liu & Jinbiao Xiong. (2023) Density-extrapolation Global Variance Reduction (DeGVR) method for large-scale radiation field calculation. Computers & Mathematics with Applications 143, pages 10-22.
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L.B. Barichello, C.B. Pazinatto & K. Rui. (2020) An analytical discrete ordinates nodal solution to the two-dimensional adjoint transport problem. Annals of Nuclear Energy 135, pages 106959.
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