A Method to Alleviate the Long History Problem Encountered in Monte Carlo Simulations via Weight Window Variance Reduction

2017 ◽  
Vol 36 (6) ◽  
pp. 204-212 ◽  
Author(s):  
Xingchen Nie ◽  
Jia Li ◽  
Yuxiao Wu ◽  
Hengquan Zhang ◽  
Songlin Liu ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Variance Reduction ◽  
Weight Window

scholarly journals PERFORMANCE STUDY OF GLOBAL WEIGHT WINDOW GENERATOR BASED ON PARTICLE DENSITY UNIFORMITY

2021 ◽  
Vol 247 ◽  
pp. 18005
Author(s):  
Peng He ◽  
Bin Wu ◽  
Lijuan Hao ◽  
Guangyao Sun ◽  
Bin Li ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Variance Reduction ◽  
Particle Density ◽  
Complex Model ◽  
Deep Penetration ◽  
Detailed Calculation ◽  
Performance Study ◽  
Global Weight ◽  
Weight Window ◽  
Calculation Results

The variance reduction techniques are necessary for Monte Carlo calculations in which obtaining a detailed calculation result for a large and complex model is required. The GVR method named as global weight window generator (GWWG) was proposed by the FDS team. In this paper, two typical calculation examples, ISPRA-Fe benchmark in SINBAD (Shielding Integral Benchmark Archive Database) and TF Coils (Toroidal Field coils) of European HCPB DEMO (Helium Cooled Pebble Bed demonstration fusion plant), are used to study the performance of GWWG method. It can be seen from the calculation results that the GWWG method has a significant effect in accelerating the Monte Carlo calculation. Especially when the global convergence calculation results are needed, the acceleration effect (FOMG) can reach 105 or more. It proves that the GWWG method is an effective tool for deep-penetration simulations using Monte Carlo method.

WE-C-108-07: Optimal Parameters for Variance Reduction in Monte Carlo Simulations for Proton Therapy

2013 ◽  
Vol 40 (6Part28) ◽  
pp. 475-475
Author(s):  
J Ramos-Mendez ◽  
J Perl ◽  
J Schuemann ◽  
J Shin ◽  
B Faddegon ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Proton Therapy ◽  
Variance Reduction ◽  
Optimal Parameters

scholarly journals On spherical Monte Carlo simulations for multivariate normal probabilities

2015 ◽  
Vol 47 (03) ◽  
pp. 817-836 ◽  
Author(s):  
Huei-Wen Teng ◽  
Ming-Hsuan Kang ◽  
Cheng-Der Fuh
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Variance Reduction ◽  
Multivariate Normal ◽  
Integration Rule ◽  
Radial Integral ◽  
Normal Probability ◽  
Antithetic Variates ◽  
Number Problem ◽  
Spherical Transformation

The calculation of multivariate normal probabilities is of great importance in many statistical and economic applications. In this paper we propose a spherical Monte Carlo method with both theoretical analysis and numerical simulation. We start by writing the multivariate normal probability via an inner radial integral and an outer spherical integral using the spherical transformation. For the outer spherical integral, we apply an integration rule by randomly rotating a predetermined set of well-located points. To find the desired set, we derive an upper bound for the variance of the Monte Carlo estimator and propose a set which is related to the kissing number problem in sphere packings. For the inner radial integral, we employ the idea of antithetic variates and identify certain conditions so that variance reduction is guaranteed. Extensive Monte Carlo simulations on some probabilities confirm these claims.

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