Random walk on semi-cylinders for diffusion problems with mixed Dirichlet–Robin boundary conditions
Sabelfeld Karl K. ()
Additional contact information
Sabelfeld Karl K.: Institute of Computational Mathematics and Mathematical Geophysics, Russian Academy of Sciences, Novosibirsk, Russia
Monte Carlo Methods and Applications, 2016, vol. 22, issue 2, 117-131
Abstract:
We suggest random walk on semi-infinite cylinders methods for solving interior and exterior diffusion problems with different types of boundary conditions which include mixed Dirichlet, Neumann, and Robin boundary conditions on different parts of the boundary. Based on probabilistic interpretation of the diffusion process, stochastic simulation algorithms take into account specific features of each boundary condition to optimally adjust the Markov chain distribution on the relevant boundary parts. In contrast to the conventional direct trajectory tracking method, the new method avoids to simulate the diffusion trajectories. Instead, it exploits exact probabilities of different events like the first passage, splitting, and survival probabilities inside the semi-infinite cylinders, depending on the domain and its boundary structure. Applications to diffusion imaging methods like the cathodoluminescence (CL) and electron beam induced current (EBIC) semiconductor analysis techniques performed in scanning electron and transmission microscopes, are discussed.
Keywords: Green's functions; random walks on semi-cylinders; Robin boundary conditions; cathodoluminescence; EBIC (search for similar items in EconPapers)
Date: 2016
References: View complete reference list from CitEc
Citations:
Downloads: (external link)
https://doi.org/10.1515/mcma-2016-0108 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:bpj:mcmeap:v:22:y:2016:i:2:p:117-131:n:5
Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/mcma/html
DOI: 10.1515/mcma-2016-0108
Access Statistics for this article
Monte Carlo Methods and Applications is currently edited by Karl K. Sabelfeld
More articles in Monte Carlo Methods and Applications from De Gruyter
Bibliographic data for series maintained by Peter Golla ().