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Title: Sparse Recovery for Scientific Data

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

Authors:
ORCiD logo [1]
  1. University of California, Los Angeles
Publication Date:
Research Org.:
Univ. of California, Los Angeles, CA (United States)
Sponsoring Org.:
USDOE Chicago Operations Office (CO)
Contributing Org.:
Oak Ridge National Laboratory
OSTI Identifier:
1561286
Report Number(s):
DE-SC0013838F
DOE Contract Number:  
SC0013838
Resource Type:
Technical Report
Resource Relation:
Related Information: Block matching local SVD operator based sparsity and TV regularization for image denoising J Liu, S Osher Journal of Scientific Computing 78 (1), 607-624, 2018
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; sparsity, total variation, compressive sensing, image reconstruction, neutron scattering

Citation Formats

Osher, Stanley. Sparse Recovery for Scientific Data. United States: N. p., 2019. Web. doi:10.2172/1561286.
Osher, Stanley. Sparse Recovery for Scientific Data. United States. doi:10.2172/1561286.
Osher, Stanley. Thu . "Sparse Recovery for Scientific Data". United States. doi:10.2172/1561286. https://www.osti.gov/servlets/purl/1561286.
@article{osti_1561286,
title = {Sparse Recovery for Scientific Data},
author = {Osher, Stanley},
abstractNote = {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},
doi = {10.2172/1561286},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {9}
}