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DOE OSTI · 1561286

Sparse Recovery for Scientific Data

Osher, Stanley J.
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Abstract

We have built build and analyzed fundamental mathematical tools that enable sparse recovery of scientific data for high performance computing, experimental facilities and sensor measurement. The proposed work will impact many areas of research across the DOE. We have designed, developed and analyzed fast scalable algorithms for the sparse recovery of scientific data sets that may be extremely large, generated very quickly and distributed across multiple storage devices. The algorithms we developed will be used to process the copious amounts of scientific data generated by the DOE supercomputer simulations and by DOE experimental and observational facilities. They leverage the fact that while the amount of data produced can be extremely large, the actual quantities of interest (QoI's) are often sparse, either in space time, or in some feature space. Mathematically, notions of compressive sensing, including l1 regularized and total variation regularized optimization have played an important part in this effort.

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97 MATHEMATICS AND COMPUTINGcompressive sensingimage reconstructionneutron scatteringsparsitytotal variation

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BibTeXRIS

Osher, Stanley J.. 2019-10-25. Sparse Recovery for Scientific Data. https://doi.org/10.2172/1561286

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