Advanced Modeling in Stochastic Computational Electromagnetics

Uncertainty Quantification in Computational Electromagnetics : The stochastic approach

— Models in electromagnetism are more and more accurate. In some applications, the gap between the experience and the model comes from the deviation on input data of the model which are not perfectly known. The stochastic approach can be used to quantify the effect of these input data uncertainties on the outputs of the model. In this article, the application of such approach in computational e...

Distributed Computational Electromagnetics Systems

We describe our development of a \real world" electromagnetic application on distributed computing systems. A computational electromagnetics (CEM) simulation for radar crosssection(RCS) modeling of full scale airborne systems has been ported to three networked workstation cluster systems: an IBM RS/6000 cluster with Ethernet connection; a DEC Alpha farm connected by a FDDI-based Gigaswitch; and...

Nedelec elements for computational electromagnetics

Time-Domain Algorithms for Computational Electromagnetics

Computational Electromagnetics and Parallel Dense Matrix Computations

We present computational results using CORAL, a parallel, three-dimensional, nonlinear magnetostatic code based on a volume integral equation formulation. A key feature of CORAL is the ability to solve, in parallel, the large, dense systems of linear equations that are inherent in the use of integral equation methods. Using the Chameleon and PSLES libraries ensures portability and access to the...