scholarly journals Application of variance reduction techniques in EGSnrc based Monte-Carlo method

2019 ◽  
Vol 22 (2) ◽  
pp. 258-263
Author(s):  
Tuan Duc Hoang ◽  
Tai Thanh Duong ◽  
Oanh Thi Luong ◽  
Loan Thi Hong Truong
Keyword(s):  
Monte Carlo ◽  
Computational Efficiency ◽  
Dose Distribution ◽  
Variance Reduction ◽  
Accurate Method ◽  
Optimal Method ◽  
Calculation Time ◽  
Variance Reduction Techniques ◽  
Reduction Techniques ◽  
Calculated Dose

Introduction: Monte Carlo (MC) is considered to be the most accurate method to calculate dose distribution in radiation therapy. However, the limitation of MC simulations is the long calculation time to reach the desired statistical uncertainty in the dose calculation as well as in clinical practice. To overcome the above limitations, Variance reduction techniques (VRTs) has developed and shorten the calculation time while maintaining accuracy. Therefore, the purpose of this study is the application of VRTs in code EGSnrc to find the optimal method for accelerator simulation and calculated dose distribution using MC method. Methods: The linear Accelerator HPD Siemens Primus at the General Hospital of Dong Nai had been simulated by using BEAMnrc code and several variance reduction techniques such as: range rejection, photon forcing, bremsstrahlung photon splitting (uniform, selective and direction)... These VRTs were used under the same set of input parameters as histories of 2x108, photon energy of 6 MV, structure, size and material of the phantom… The computational efficiency ε is calculated by the following equation ε = 1/T.σ2 where T is the CUP time of calculation and  σ2 is an estimate of the variance, for evaluating and selecting the VRT which gives the best computational efficiency. Results: The results showed a good agreement between the calculated dose and measured ones when applying different VRTs. These techniques were significantly reduced uncertainty in simulation compared the analog cases. Specifically, the efficiency of DBS and UBS improved by more than 90 times and 15 times compared with the analog instances, respectively. Rang rejection and photon forcing techniques also haveimproved the efficiency of simulation, but not significantly. Conclusions: The application of the VRTs for EGSnrc increase the efficiency of the simulation. VRTs is a powerful tool that should be applied for the simulation by code EGSnrc to improve calculation efficiency by reducing simulation time and its variance. Our results show that the direction bremsstrahlung splitting (DBS) gives thebest computational efficiency.  

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.

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.

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