Stochastic Finite-difference Time-domain Research Articles

A 3-D Stochastic FDTD Model of Electromagnetic Wave Propagation in Magnetized Ionosphere Plasma

A stochastic finite-difference time-domain (S-FDTD) algorithm is presented for electromagnetic-wave propagation in anisotropic magnetized plasma. This new algorithm efficiently calculates in a single simulation not only the mean electromagnetic field values, but also their variance as caused by the variability or uncertainty of the electron and ion content of the ionosphere. The structure of the ionosphere is often too variable and uncertain for electromagnetic-wave propagation problems to be solved using a deterministic formulation, particularly during space weather events. For these cases, the S-FDTD ionospheric plasma algorithm will serve as an important tool. For example, it could be used to determine the confidence level at which a communications or remote sensing or radar system will operate as expected under abnormal ionospheric conditions.

Single Realization Stochastic FDTD for Weak Scattering Waves in Biological Random Media

This paper introduces an iterative scheme to overcome the unresolved issues presented in S-FDTD (stochastic finite-difference time-domain) for obtaining ensemble average field values recently reported by Smith and Furse in an attempt to replace the brute force multiple-realization also known as Monte-Carlo approach with a single-realization scheme. Our formulation is particularly useful for studying light interactions with biological cells and tissues having sub-wavelength scale features. Numerical results demonstrate that such a small scale variation can be effectively modeled with a random medium problem which when simulated with the proposed S-FDTD indeed produces a very accurate result.

Stochastic FDTD for Analysis of Statistical Variation in Electromagnetic Fields

This paper describes a new stochastic finite difference time domain (S-FDTD) method for calculating the variance in the electromagnetic fields caused by variability or uncertainty in the electrical properties of the materials in the model. Details of the 1D derivation using truncated Taylor series approximations are given. The S-FDTD analysis is then compared to Monte Carlo analysis for a 1D bioelectromagnetic example. The accuracy of the method is controlled by the approximations for the cross correlations of the fields and electrical properties. In this paper, we are able to bound the variance using cross correlations of 1 (overestimate) and the reflection coefficient (underestimate).