Scientific data interpolation with low dimensional manifold model

Zhu, Wei; Wang, Bao; Barnard, Richard C.; Hauck, Cory D.; Jenko, Frank; Osher, Stanley

doi:10.1016/j.jcp.2017.09.048

Title: Scientific data interpolation with low dimensional manifold model

Journal Article · Thu Sep 28 00:00:00 EDT 2017 · Journal of Computational Physics

DOI:https://doi.org/10.1016/j.jcp.2017.09.048· OSTI ID:1407782

^[1]; Wang, Bao ^[1];

^[2];

^[3]; Jenko, Frank ^[1]; Osher, Stanley ^[1]

Univ. of California Los Angeles, Los Angeles, CA (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Univ. of Tennessee, Knoxville, TN (United States)

Here, we propose to apply a low dimensional manifold model to scientific data interpolation from regular and irregular samplings with a significant amount of missing information. The low dimensionality of the patch manifold for general scientific data sets has been used as a regularizer in a variational formulation. The problem is solved via alternating minimization with respect to the manifold and the data set, and the Laplace–Beltrami operator in the Euler–Lagrange equation is discretized using the weighted graph Laplacian. Various scientific data sets from different fields of study are used to illustrate the performance of the proposed algorithm on data compression and interpolation from both regular and irregular samplings.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)

Grant/Contract Number:: AC05-00OR22725; De-AC05-00OR22725; DOE-SC0013838

OSTI ID:: 1407782

Alternate ID(s):: OSTI ID: 1549289

Journal Information:: Journal of Computational Physics, Vol. 352, Issue C; ISSN 0021-9991

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 7 works

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Low dimensional manifold model (LDMM)
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