Analysis of the LIFT Variance-Reduction Method Applied to Monte Carlo Radiation Transport Simulations of a Realistic Nonproliferation Test Problem

2016 ◽  
Vol 193 (3) ◽  
pp. 391-403
Author(s):  
Elanchezhian Somasundaram ◽  
Todd S. Palmer
Keyword(s):  
Monte Carlo ◽  
Test Problem ◽  
Variance Reduction ◽  
Reduction Method ◽  
Radiation Transport

scholarly journals Stratification as a General Variance Reduction Method for Markov Chain Monte Carlo

2020 ◽  
Vol 8 (3) ◽  
pp. 1139-1188
Author(s):  
Aaron R. Dinner ◽  
Erik H. Thiede ◽  
Brian Van Koten ◽  
Jonathan Weare
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Variance Reduction ◽  
Reduction Method

Global variance reduction method based on multi-group Monte Carlo adjoint calculation

2021 ◽  
Vol 151 ◽  
pp. 107958
Author(s):  
Tao Shi ◽  
Hui Li ◽  
Qianxue Ding ◽  
Mengqi Wang ◽  
Zheng Zheng ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Variance Reduction ◽  
Reduction Method ◽  
Global Variance Reduction

scholarly journals Monte Carlo model and variance reduction method based on lidar of ship wake

2013 ◽  
Vol 62 (1) ◽  
pp. 015205
Author(s):  
Liang Shan-Yong ◽  
Wang Jiang-An ◽  
Zhang Feng ◽  
Wu Rong-Hua ◽  
Zong Si-Guang ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Variance Reduction ◽  
Reduction Method ◽  
Monte Carlo Model ◽  
Ship Wake

scholarly journals Variance-Reduction Methods for Monte Carlo Simulation of Radiation Transport

2021 ◽  
Vol 9 ◽  
Author(s):  
Salvador García-Pareja ◽  
Antonio M. Lallena ◽  
Francesc Salvat
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Variance Reduction ◽  
Radiation Transport ◽  
Scattering Method ◽  
Variance Reduction Techniques ◽  
Simulation Efficiency ◽  
Reduction Techniques ◽  
Exponential Transform ◽  
Reduction Methods

After a brief description of the essentials of Monte Carlo simulation methods and the definition of simulation efficiency, the rationale for variance-reduction techniques is presented. Popular variance-reduction techniques applicable to Monte Carlo simulations of radiation transport are described and motivated. The focus is on those techniques that can be used with any transport code, irrespective of the strategies used to track charged particles; they operate by manipulating either the number and weights of the transported particles or the mean free paths of the various interaction mechanisms. The considered techniques are 1) splitting and Russian roulette, with the ant colony method as builder of importance maps, 2) exponential transform and interaction-forcing biasing, 3) Woodcock or delta-scattering method, 4) interaction forcing, and 5) proper use of symmetries and combinations of different techniques. Illustrative results from analog simulations (without recourse to variance-reduction) and from variance-reduced simulations of various transport problems are presented.

Monte Carlo Simulation of Characteristic Secondary Fluorescence in Electron Probe Microanalysis of Homogeneous Samples Using the Splitting Technique

2015 ◽  
Vol 21 (3) ◽  
pp. 753-758 ◽  
Author(s):  
Mauricio Petaccia ◽  
Silvina Segui ◽  
Gustavo Castellano
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Electron Probe Microanalysis ◽  
Electron Probe ◽  
Variance Reduction ◽  
Fluorescence Enhancement ◽  
Radiation Transport ◽  
X Rays ◽  
X Ray ◽  
Secondary Fluorescence

AbstractElectron probe microanalysis (EPMA) is based on the comparison of characteristic intensities induced by monoenergetic electrons. When the electron beam ionizes inner atomic shells and these ionizations cause the emission of characteristic X-rays, secondary fluorescence can occur, originating from ionizations induced by X-ray photons produced by the primary electron interactions. As detectors are unable to distinguish the origin of these characteristic X-rays, Monte Carlo simulation of radiation transport becomes a determinant tool in the study of this fluorescence enhancement. In this work, characteristic secondary fluorescence enhancement in EPMA has been studied by using the splitting routines offered by PENELOPE 2008 as a variance reduction alternative. This approach is controlled by a single parameter NSPLIT, which represents the desired number of X-ray photon replicas. The dependence of the uncertainties associated with secondary intensities on NSPLIT was studied as a function of the accelerating voltage and the sample composition in a simple binary alloy in which this effect becomes relevant. The achieved efficiencies for the simulated secondary intensities bear a remarkable improvement when increasing the NSPLIT parameter; although in most cases an NSPLIT value of 100 is sufficient, some less likely enhancements may require stronger splitting in order to increase the efficiency associated with the simulation of secondary intensities.

Global variance reduction method for global Monte Carlo particle transport simulations of CFETR

2017 ◽  
Vol 28 (8) ◽  
Author(s):  
Xing-Chen Nie ◽  
Jia Li ◽  
Song-Lin Liu ◽  
Xiao-Kang Zhang ◽  
Ping-Hui Zhao ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Particle Transport ◽  
Variance Reduction ◽  
Reduction Method ◽  
Global Variance Reduction

scholarly journals ANALISIS RISIKO PORTOFOLIO MENGGUNAKAN METODE SIMULASI MONTE CARLO CONTROL VARIATES

2021 ◽  
Vol 10 (4) ◽  
pp. 192
Author(s):  
IRENE MAYLINDA PANGARIBUAN ◽  
KOMANG DHARMAWAN ◽  
I WAYAN SUMARJAYA
Keyword(s):  
At Risk ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Value At Risk ◽  
Variance Reduction ◽  
Reduction Method ◽  
Random Numbers ◽  
Control Variates ◽  
Popular Method ◽  
Maximum Loss

Value at Risk (VaR) is a method to measure the maximum loss with a certain level of confidence in a certain period. Monte Carlo simulation is the most popular method of calculating VaR. The purpose of this study is to demonstrate control variates method as a variance reduction method that can be applied to estimate VaR. Moreover, it is to compare the results with the normal VaR method or analytical VaR calculation. Control variates method was used to find new returns from all stocks which are used as estimators of the control variates. The new returns were then used to define parameters needed to generate N random numbers. Furthermore, the generated numbers were used to find the VaR value. The method was then applied to estimate a portfolio of the game and esports company stocks that are EA, TTWO, AESE, TCEHY, and ATVI . The results show Monte Carlo simulation gives VaR of US$41.6428 within 1000 simulation, while the analytical VaR calculation  or  normal VaR method gives US$30.0949.

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