Comparison of a semi-analytic variance reduction technique to classical Monte Carlo variance reduction techniques for high aspect ratio pencil beam collimators for emission tomography applications

2021 ◽  
pp. 165236
Author(s):  
Seth Kilby ◽  
Jack Fletcher ◽  
Zhongmin Jin ◽  
Ashish Avachat ◽  
Devin Imholte ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Aspect Ratio ◽  
High Aspect Ratio ◽  
Variance Reduction ◽  
Reduction Technique ◽  
Emission Tomography ◽  
Pencil Beam ◽  
Variance Reduction Techniques ◽  
Variance Reduction Technique ◽  
Reduction Techniques

A source biasing and variance reduction technique for Monte Carlo radiation transport modeling of emission tomography problems

2019 ◽  
Vol 320 (1) ◽  
pp. 37-45 ◽  
Author(s):  
Seth Kilby ◽  
Zhongmin Jin ◽  
Ashish Avachat ◽  
Bryant Kanies ◽  
Nicholas Woolstenhulme ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Variance Reduction ◽  
Radiation Transport ◽  
Reduction Technique ◽  
Transport Modeling ◽  
Emission Tomography ◽  
Variance Reduction Technique

scholarly journals Some variance reduction methods for numerical stochastic homogenization

2016 ◽  
Vol 374 (2066) ◽  
pp. 20150168 ◽  
Author(s):  
X. Blanc ◽  
C. Le Bris ◽  
F. Legoll
Keyword(s):  
Monte Carlo ◽  
Variance Reduction ◽  
Stochastic Homogenization ◽  
Numerical Homogenization ◽  
Variance Reduction Techniques ◽  
Reduction Techniques ◽  
Effective Coefficients ◽  
Engineering Sciences ◽  
Reduction Methods ◽  
The Cost

We give an overview of a series of recent studies devoted to variance reduction techniques for numerical stochastic homogenization. Numerical homogenization requires that a set of problems is solved at the microscale, the so-called corrector problems. In a random environment, these problems are stochastic and therefore need to be repeatedly solved, for several configurations of the medium considered. An empirical average over all configurations is then performed using the Monte Carlo approach, so as to approximate the effective coefficients necessary to determine the macroscopic behaviour. Variance severely affects the accuracy and the cost of such computations. Variance reduction approaches, borrowed from other contexts in the engineering sciences, can be useful. Some of these variance reduction techniques are presented, studied and tested here.

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