Retrieval of Similar Objects in Simulation Data Using Machine Learning Techniques

Cantu-Paz, E; Cheung, S-C; Kamath, C

doi:10.1117/12.527122

Title: Retrieval of Similar Objects in Simulation Data Using Machine Learning Techniques

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Abstract

Comparing the output of a physics simulation with an experiment is often done by visually comparing the two outputs. In order to determine which simulation is a closer match to the experiment, more quantitative measures are needed. This paper describes our early experiences with this problem by considering the slightly simpler problem of finding objects in a image that are similar to a given query object. Focusing on a dataset from a fluid mixing problem, we report on our experiments using classification techniques from machine learning to retrieve the objects of interest in the simulation data. The early results reported in this paper suggest that machine learning techniques can retrieve more objects that are similar to the query than distance-based similarity methods.

Authors:: Cantu-Paz, E; Cheung, S-C; Kamath, C

Publication Date:: Thu Jun 19 00:00:00 EDT 2003

Research Org.:: Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 15004415

Report Number(s):: UCRL-JC-153866
Journal ID: ISSN 0277-786X; TRN: US201015%%591

DOE Contract Number:: W-7405-ENG-48

Resource Type:: Conference

Resource Relation:: Journal Volume: 5298; Conference: Electronic Imaging, San Jose, CA, Jan 20 - Jan 24, 2003

Country of Publication:: United States

Language:: English

Subject:: 42; 99; CLASSIFICATION; FOCUSING; LEARNING; PHYSICS; SIMULATION

Citation Formats


                    Cantu-Paz, E, Cheung, S-C, and Kamath, C. Retrieval of Similar Objects in Simulation Data Using Machine Learning Techniques.  United States: N. p., 2003. 
        Web.  doi:10.1117/12.527122.

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                    Cantu-Paz, E, Cheung, S-C, & Kamath, C. Retrieval of Similar Objects in Simulation Data Using Machine Learning Techniques.  United States.  https://doi.org/10.1117/12.527122

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                    Cantu-Paz, E, Cheung, S-C, and Kamath, C. 2003.  
        "Retrieval of Similar Objects in Simulation Data Using Machine Learning Techniques".  United States.  https://doi.org/10.1117/12.527122.  https://www.osti.gov/servlets/purl/15004415.

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@article{osti_15004415,

  title        = {Retrieval of Similar Objects in Simulation Data Using Machine Learning Techniques},

  author       = {Cantu-Paz, E and Cheung, S-C and Kamath, C},

  abstractNote = {Comparing the output of a physics simulation with an experiment is often done by visually comparing the two outputs. In order to determine which simulation is a closer match to the experiment, more quantitative measures are needed. This paper describes our early experiences with this problem by considering the slightly simpler problem of finding objects in a image that are similar to a given query object. Focusing on a dataset from a fluid mixing problem, we report on our experiments using classification techniques from machine learning to retrieve the objects of interest in the simulation data. The early results reported in this paper suggest that machine learning techniques can retrieve more objects that are similar to the query than distance-based similarity methods.},

  doi          = {10.1117/12.527122},

  url          = {https://www.osti.gov/biblio/15004415},
  journal      = {},

  issn         = {0277-786X},

  number       = ,

  volume       = 5298,

  place        = {United States},

  year         = {Thu Jun 19 00:00:00 EDT 2003},

  month        = {Thu Jun 19 00:00:00 EDT 2003}

}

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