Abstract-In this paper, a technique is introduced by which to extend the applicability of the existing analytic solutions of electromagnetic field problems to cases where random-noisy-sampled data (such as measurement outputs) are available, rather than analytic input functions. We address those

tion will be generalized to the use of the compatibility relations for electromagnetic fields. It will also be shown that the use of divergence-free edge elements, which is advocated by some authors (see

Much of today’s finite elements-based software for electromagnetic field computation has been developed on an ad hoc basis, often as an outgrowth of ongoing research whether at universities or agencies like NASA. “Design ” is seen as the execution of computational algorithms yielding the computed

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–Seidel on CMPs by using a new cache blocking technique to overcome the small cache problem while using a thread synchronization technique for better cache sharing and to maximize thread cycle utilization. Index Terms—Cache blocking, chip multicore processors (CMPs), electromagnetic field problem solver, Gauss

Abstract. This paper is a survey of the inverse scattering problem for time-harmonic acoustic and electromagnetic waves at fixed frequency. We begin by a discussion of “weak scattering ” and Newton-type methods for solving the inverse scattering problem for acoustic waves, including a brief

are investi- [2] T. Tamir and A. A. Oliner, “The spectrum of electromagnetic waves guided by a plasma layer, ” Proc. IEEE, vol. 51, pp. 317gated for the case in which the static magnetic field is 332, February 1963. oriented perpendicular to the plane interface. It is [3] &I. A. Gintsburg, “Surface waves

Finite element-based electromagnetic field simulation strongly benefits from using edge-conforming representations of the electric field. In this paper we address the visualization of discrete field data resulting from such simulations on 10-node quadratic tetrahedral grids. The use of higher

We present a technique for constructing random fields from a set of training samples. The learning paradigm builds increasingly complex fields by allowing potential functions, or features, that are supported by increasingly large subgraphs. Each feature has a weight that is trained by minimizing

. The latter relates to how data is observed and is problem domain dependent. The former depends on how various prior constraints are expressed. Markov Random Field Models (MRF) theory is a tool to encode contextual constraints into the prior probability. This paper presents a unified approach for MRF modeling