Complex optimization for big computational and experimental neutron datasets

Feng Bao; Richard Archibald; Jennifer Niedziela; Dipanshu Bansal; Olivier Delaire

doi:10.1088/0957-4484/27/48/484002

Complex optimization for big computational and experimental neutron datasets

Feng Bao, Richard Archibald, Jennifer Niedziela, Dipanshu Bansal, Olivier Delaire

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

We present a framework to use high performance computing to determine accurate solutions to the inverse optimization problem of big experimental data against computational models. We demonstrate how image processing, mathematical regularization, and hierarchical modeling can be used to solve complex optimization problems on big data. We also demonstrate how both model and data information can be used to further increase solution accuracy of optimization by providing confidence regions for the processing and regularization algorithms. We use the framework in conjunction with the software package SIMPHONIES to analyze results from neutron scattering experiments on silicon single crystals, and refine first principles calculations to better describe the experimental data.

Original language	English
Article number	484002
Journal	Nanotechnology
Volume	27
Issue number	48
DOIs	https://doi.org/10.1088/0957-4484/27/48/484002
State	Published - Nov 7 2016

Funding

Acknowledges support by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division through the Office of Science Early Career Research Program and Advanced Scientific Computing Research, through the ACUMEN project.

Funders	Funder number
U.S. Department of Energy
Office of Science
Basic Energy Sciences
Division of Materials Sciences and Engineering

Keywords

hierarchical optimization
image processing
inelastic neutron scattering
mathematical regularization

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10.1088/0957-4484/27/48/484002

Cite this

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title = "Complex optimization for big computational and experimental neutron datasets",

abstract = "We present a framework to use high performance computing to determine accurate solutions to the inverse optimization problem of big experimental data against computational models. We demonstrate how image processing, mathematical regularization, and hierarchical modeling can be used to solve complex optimization problems on big data. We also demonstrate how both model and data information can be used to further increase solution accuracy of optimization by providing confidence regions for the processing and regularization algorithms. We use the framework in conjunction with the software package SIMPHONIES to analyze results from neutron scattering experiments on silicon single crystals, and refine first principles calculations to better describe the experimental data.",

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AU - Bao, Feng

AU - Archibald, Richard

AU - Niedziela, Jennifer

AU - Bansal, Dipanshu

AU - Delaire, Olivier

PY - 2016/11/7

Y1 - 2016/11/7

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AB - We present a framework to use high performance computing to determine accurate solutions to the inverse optimization problem of big experimental data against computational models. We demonstrate how image processing, mathematical regularization, and hierarchical modeling can be used to solve complex optimization problems on big data. We also demonstrate how both model and data information can be used to further increase solution accuracy of optimization by providing confidence regions for the processing and regularization algorithms. We use the framework in conjunction with the software package SIMPHONIES to analyze results from neutron scattering experiments on silicon single crystals, and refine first principles calculations to better describe the experimental data.

KW - hierarchical optimization

KW - image processing

KW - inelastic neutron scattering

KW - mathematical regularization

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Complex optimization for big computational and experimental neutron datasets

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Keywords

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