Conditional Point Sampling: A stochastic media transport algorithm with full geometric sampling memory

Vu, Emily; Olson, Aaron

doi:10.1016/j.jqsrt.2021.107767

Cited by 6 publications

(2 citation statements)

References 13 publications

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“…The idea of the variance deconvolution, in RT applications, was previously introduced in [4] and presented in the context of an embedded UQ strategy dubbed Embedded VAriance DEconvolution (EVADE). Moreover, EVADE has been successfully adopted in RT computations in the presence of stochastic media, as in [5]. The original EVADE estimator presented in [4] was derived for an approximation of Q obtained with a single particle history.…”

Section: Sand2022-3023cmentioning

confidence: 99%

Numerical Investigation on the Performance of a Variance Deconvolution Estimator.

Clements,

Geraci,

Olson

2022

Proposed for Presentation at the 2022 SIAM Conference on Uncertainty Quantification Held April 12-15, 2022 in Atlanta, Georgia

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Section: Sand2022-3023cmentioning

confidence: 99%

Numerical Investigation on the Performance of a Variance Deconvolution Estimator.

Clements,

Geraci,

Olson

2022

Proposed for Presentation at the 2022 SIAM Conference on Uncertainty Quantification Held April 12-15, 2022 in Atlanta, Georgia

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Section: Sand2022-3023cmentioning

confidence: 99%

Numerical Investigation on the Performance of a Variance Deconvolution Estimator.

Clements,

Geraci,

Olson

2022

Proposed for Presentation at the ANS Annual Meeting Held June 12-17, 2022 in Anaheim, California United States

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Evaluation of memory-preserving models for particle transport in random media

Samba,

Charpentier,

Stepanovic

et al. 2024

EPJ Web Conf.

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A shortcoming of the Levermore-Pomraning (LP) model is its inaccuracy to treat the transport of particles in a diffusive material alternating with a transparent one. Enhanced-memory LP models have been proposed by Zimermann et al. (1991) and Larmier (2018) to circumvent this issue. The main purpose of this paper is to evaluate the PBS2 model also known as Algorithm C in these geometries. A second objective is to generalize it to an arbitrary number of interfaces allowing the same code to give either an approximation of the solution with a few interfaces or a reference result by increasing their number. A third one is to give a mathematical formulation of these models.

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Conditional Point Sampling: A stochastic media transport algorithm with full geometric sampling memory

Cited by 6 publications

References 13 publications

Numerical Investigation on the Performance of a Variance Deconvolution Estimator.

Numerical Investigation on the Performance of a Variance Deconvolution Estimator.

Numerical Investigation on the Performance of a Variance Deconvolution Estimator.

Evaluation of memory-preserving models for particle transport in random media

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