Ravi Srinivasan

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We study shock statistics in the scalar conservation law ∂tu+∂xf (u) = 0, x ∈ R, t > 0, with a convex flux f and random initial data. We show that a large class of random initial data (Markov processes with downward jumps and derivatives of Lévy processes with downward jumps) is preserved by the entropy solution to the conservation law and we derive kinetic(More)
As infectious disease surveillance systems expand to include digital, crowd-sourced, and social network data, public health agencies are gaining unprecedented access to high-resolution data and have an opportunity to selectively monitor informative individuals. Contact networks, which are the webs of interaction through which diseases spread, determine(More)
Adapting legacy applications for use in a modern heterogeneous environment is a serious challenge for an industrial software vendor (ISV). The adaptation of the NUMECA FINE/Turbo computational fluid dynamics (CFD) solver for accelerated CPU/GPU execution is presented. An incremental instrumentation with OpenACC directives has been used to obtain a global(More)
We establish nearly optimal rates of convergence to self-similar solutions of Smolu-chowski's coagulation equation with kernels K = 2, x + y, and xy. The method is a simple analogue of the Berry-Esséen theorems in classical probability and requires minimal assumptions on the initial data, namely that of an extra finite moment condition. For each kernel it(More)
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