Grid Knowledge Discovery Processes and an Architecture for Their Composition

The Grid is the computing and data management infrastructure, which is transforming science, business, health and society. This paper deals with a challenging task addressing knowledge discovery in data repositories within the Grid. It gives an overview about the architecture of a novel Grid knowledge discovery system, the collaboration of the designed services and the concepts of controlling the whole knowledge discovery process by the deployment of a workflow component

Adaptive Domain Decomposition Methods for Finite and Boundary Element August 1995 Equations.

A Mixed Variational Formulation for 3D Magnetostatics and its Finite Element February 199

Automatically generated, anatomically accurate meshes for cardiac electrophysiology problems.

Significant advancements in imaging technology and the dramatic increase in computer power over the last few years broke the ground for the construction of anatomically realistic models of the heart at an unprecedented level of detail. To effectively make use of high-resolution imaging datasets for modeling purposes, the imaged objects have to be discretized. This procedure is trivial for structured grids. However, to develop generally applicable heart models, unstructured grids are much preferable. In this study, a novel image-based unstructured mesh generation technique is proposed. It uses the dual mesh of an octree applied directly to segmented 3-D image stacks. The method produces conformal, boundary-fitted, and hexahedra-dominant meshes. The algorithm operates fully automatically with no requirements for interactivity and generates accurate volume-preserving representations of arbitrarily complex geometries with smooth surfaces. The method is very well suited for cardiac electrophysiological simulations. In the myocardium, the algorithm minimizes variations in element size, whereas in the surrounding medium, the element size is grown larger with the distance to the myocardial surfaces to reduce the computational burden. The numerical feasibility of the approach is demonstrated by discretizing and solving the monodomain and bidomain equations on the generated grids for two preparations of high experimental relevance, a left ventricular wedge preparation, and a papillary muscle