Showing papers on "Dynamic Monte Carlo method published in 2019"
TL;DR: In this paper, a numerical method integrated with statistical data simulation technique is introduced to solve a nonlinear system of ordinary differential equations with multiple random variable (RV) variables. But, the method is not suitable for solving the problem with multiple variables.
Abstract: In this article, a numerical method integrated with statistical data simulation technique is introduced to solve a nonlinear system of ordinary differential equations with multiple random variable ...
17 citations
TL;DR: In this article, the authors used path integral Monte Carlo (PIMC) simulations to calculate the momentum distribution of the homogeneous electron gas at finite temperature and demonstrated how the restricted PIMC method can be extended to incorporate open paths in order to allow for simulations in fermionic systems where a sign problem is present.
17 citations
TL;DR: In this paper, the authors proposed a hybrid finite difference method with a Monte Carlo boundary condition for solving the Black-Scholes equations, which can be applied to other types of option pricing problems.
Abstract: We propose an accurate, efficient, and robust hybrid finite difference method, with a Monte Carlo boundary condition, for solving the Black–Scholes equations. The proposed method uses a far-field boundary value obtained from a Monte Carlo simulation, and can be applied to problems with non-linear payoffs at the boundary location. Numerical tests on power, powered, and two-asset European call option pricing problems are presented. Through these numerical simulations, we show that the proposed boundary treatment yields better accuracy and robustness than the most commonly used linear boundary condition. Furthermore, the proposed hybrid method is general, which means it can be applied to other types of option pricing problems. In particular, the proposed Monte Carlo boundary condition algorithm can be implemented easily in the code of the existing finite difference method, with a small modification.
16 citations
TL;DR: A sequential Monte Carlo version of the approach is developed and it is shown under some assumptions that for a given level of mean square error, this method for ABC has a lower cost than i.i.d. sampling from the most accurate ABC approximation.
Abstract: In the following article, we consider approximate Bayesian computation (ABC) inference. We introduce a method for numerically approximating ABC posteriors using the multilevel Monte Carlo (MLMC). A...
13 citations
20 May 2019
3 citations
TL;DR: In this article, a new time-dependent transport approach is described to simulate the nuclear reactor dynamic correctly using the MCNPX code, and the fundamental parameters of a nuclear reactor like multiplication factor (Keff) and mean generation time (tG) are calculated using MC NPX code.
3 citations
TL;DR: In this article, conductive atomic force microscopy, C-AFM, is used to improve performance of organic photovoltaics, and further understanding is required. But, it is difficult to apply it in the real world.
Abstract: Morphology is one of key factors to improve performance of organic photovoltaics, and further understanding is required. Conductive atomic force microscopy, C-AFM, is an experimental technique that...