A Deep Learning Approach to Identifying Shock Locations in Turbulent Combustion Tensor Fields

  • Mathew Monfort
  • Timothy Luciani
  • Jonathan Komperda
  • Brian Ziebart
  • Farzad Mashayek
  • G. Elisabeta Marai
Conference paper
Part of the Mathematics and Visualization book series (MATHVISUAL)

Abstract

We introduce a deep learning approach for the identification of shock locations in large scale tensor field datasets. Such datasets are typically generated by turbulent combustion simulations. In this proof of concept approach, we use deep learning to learn mappings from strain tensors to Schlieren images which serve as labels. The use of neural networks allows for the Schlieren values to be approximated more efficiently than calculating the values from the density gradient. In addition, we show that this approach can be used to predict the Schlieren values for both two-dimensional and three-dimensional tensor fields, potentially allowing for anomaly detection in tensor flows. Results on two shock example datasets show that this approach can assist in the extraction of features from reacting flow tensor fields.

Notes

Acknowledgements

This work was partially supported by the National Science Foundation through award NSF CAREER IIS-1541277. We gratefully acknowledge the NSF Graduate Research Fellowship program for supporting Tim. We thank Adrian Maries and Shiwangi Singh for the initial literature search, and the Electronic Visualization Lab for their feedback and support. We further thank the Peyman Lab for the original motivation behind this line of work, and the Dagstuhl 16142 and 14082 seminars run by the Leibniz Center for Informatics for the many useful discussions regarding tensor field analysis and visualization .

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Mathew Monfort
    • 1
  • Timothy Luciani
    • 1
  • Jonathan Komperda
    • 1
  • Brian Ziebart
    • 1
  • Farzad Mashayek
    • 1
  • G. Elisabeta Marai
    • 1
  1. 1.University of Illinois at ChicagoChicagoUSA

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