A stochastic method is developed, implemented and investigated here for solving Laplace, Poisson\u27s, and standard parabolic wave equations. This method is based on the properties of random walk, diffusion process, Ito formula, Dynkin formula and Monte Carlo simulations. The developed method is a local method i:e: it gives the value of the solution directly at an arbitrary point rather than extracting its value from complete field solution and thus is inherently parallel. Field computation by this method is demonstrated for electrostatic and electrodynamic propagation problems by considering simple examples and numerical results are presented to validate this method. Numerical investigations are carried out to understand efficacy and limit...
This thesis discusses and develops one approach to solve parabolic partial differential equations wi...
A robust semi-implicit central partial difference algorithm for the numerical solution of coupled st...
A novel nonintrusive statistical approach, known as the stochastic reduced order model (SROM) method...
A stochastic method is developed, implemented and investigated here for solving Laplace, Poisson\u27...
Abstract Purpose – The purpose of this paper is to demonstrate how Monte Carlo methods can be applie...
Using a computational method to solve parabolic differential equations is a feat that can be done wi...
This thesis presents a set of tools and methodologies that perform fast stochastic characterization ...
This dissertation study three different approaches for stochastic electromagnetic fields based on th...
This book gives a comprehensive introduction to numerical methods and analysis of stochastic process...
We apply stochastic techniques towards the solution of the two-dimensional Laplace\u27s equation in ...
The stochastic computation of electromagnetic (EM) problems is a relatively new topic, yet very impo...
As speed and density of electronic systems continue to increase, uncertainties are becoming signific...
Efficient computation in deterministic and uncertain electromagnetic propagation environments, tackl...
In this thesis, we consider four different stochastic partial differential equations. Firstly, we st...
Partial differential equations (PDEs) and stochastic partial differential equations (SPDEs) are powe...
This thesis discusses and develops one approach to solve parabolic partial differential equations wi...
A robust semi-implicit central partial difference algorithm for the numerical solution of coupled st...
A novel nonintrusive statistical approach, known as the stochastic reduced order model (SROM) method...
A stochastic method is developed, implemented and investigated here for solving Laplace, Poisson\u27...
Abstract Purpose – The purpose of this paper is to demonstrate how Monte Carlo methods can be applie...
Using a computational method to solve parabolic differential equations is a feat that can be done wi...
This thesis presents a set of tools and methodologies that perform fast stochastic characterization ...
This dissertation study three different approaches for stochastic electromagnetic fields based on th...
This book gives a comprehensive introduction to numerical methods and analysis of stochastic process...
We apply stochastic techniques towards the solution of the two-dimensional Laplace\u27s equation in ...
The stochastic computation of electromagnetic (EM) problems is a relatively new topic, yet very impo...
As speed and density of electronic systems continue to increase, uncertainties are becoming signific...
Efficient computation in deterministic and uncertain electromagnetic propagation environments, tackl...
In this thesis, we consider four different stochastic partial differential equations. Firstly, we st...
Partial differential equations (PDEs) and stochastic partial differential equations (SPDEs) are powe...
This thesis discusses and develops one approach to solve parabolic partial differential equations wi...
A robust semi-implicit central partial difference algorithm for the numerical solution of coupled st...
A novel nonintrusive statistical approach, known as the stochastic reduced order model (SROM) method...