Retrieval of Similar Objects in Simulation Data Using Machine Learning Techniques
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.
- Research Organization:
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- W-7405-ENG-48
- OSTI ID:
- 15004415
- Report Number(s):
- UCRL-JC-153866; TRN: US201015%%591
- Resource Relation:
- Journal Volume: 5298; Conference: Electronic Imaging, San Jose, CA, Jan 20 - Jan 24, 2003
- Country of Publication:
- United States
- Language:
- English
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