Juliana Kwan

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Mesh tessellations are indispensable tools for analyzing point data because they transform sparse discrete samples into dense continuous functions. Meshing the output of petascale simulations, however, can be as data-intensive as the simulations themselves and often must be executed in parallel on the same supercomputers in order to fit in memory. To date,(More)
Large-volume sky surveys have accessed the Universe's vast temporal and spatial expanse via a remarkable set of measurements, and many more are sure to follow. To make new predictions for these cosmological observations and to properly interpret them, large-scale numerical simulation and modeling has become an essential tool. Here, the authors discuss(More)
The Fractal Bubble model has been proposed as a viable cosmology that does not require dark energy to account for cosmic acceleration, but rather attributes its observational signature to the formation of structure. In this paper it is demonstrated that, in contrast to previous findings, this model is not a good fit to cosmological supernovae data; there is(More)
Mesh tessellations are effective constructs for the visualization and analysis of point data, because they transform sparse discrete samples into dense and continuous functions. We present a prototype method for computing a Voronoi tessellation in parallel from large particle datasets; the same method, in principle, is applicable to the Delaunay. Computing(More)
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