Michael A. Matheson

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Applications of large-scale computer modeling and simulation to a class of bioelectric field problems in a variety of biomedical research areas, from single cells to full human structures, are described. The authors focus on a specific subset of bioelectric field problems that have applications in electrocardiography and electroencephalography. The physics(More)
We describe a project in the field of computational electrocardiology which requires visualization of complex, three-dimensional geometry and electric potential and current fields. Starting from magnetic resonance images from a healthy subject, we constructed a multisurfaced model of the human thorax, which we used as the basis for computational studies(More)
A toolkit developed to visualize cardiac electrophysiology is discussed. The geometric models that play a crucial role in the analysis, manipulation, and visualization of cardiac bioelectric data and the two separate data visualization systems developed for quick, flexible viewing of spatially distributed data and for displaying selected frames of data or(More)
This visualization shows the results of the simulation of a Mach 4 flow into two cone nosed cylindrical bodies adjacent to a flat plate. The analysis was computed with a Reynolds Averaged Navier Stokes (RANS) code utilizing a Spalart-Allmaras Turbulent closure. The computational solution was conducted on a high resolution grid of one billion cells to allow(More)
Scaling-up scientific data analysis and machine learning algorithms for data-driven discovery is a grand challenge that we face today. Despite the growing need for analysis from science domains that are generating ‘Big Data’ from instruments and simulations, building high-performance analytical workflows of data-intensive algorithms have been(More)
In this paper, we pose and address some of the unique challenges in the analysis of scientific Big Data on supercomputing platforms. Our approach identifies, implements and scales numerical kernels that are critical to the instantiation of theory-inspired analytic workflows on modern computing architectures. We present the benefits of scalable kernels(More)
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