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Modeling, Analysis, and Visualization of Anisotropy pp 375–392Cite as

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

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Part of the book series: Mathematics and Visualization ((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.

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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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Authors and Affiliations

  1. University of Illinois at Chicago, Chicago, IL, 60607, USA

    Mathew Monfort, Timothy Luciani, Jonathan Komperda, Brian Ziebart, Farzad Mashayek & G. Elisabeta Marai

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  1. Mathew Monfort
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  2. Timothy Luciani
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  6. G. Elisabeta Marai
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Correspondence to G. Elisabeta Marai .

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Editors and Affiliations

  1. Institute of Computer Science, University of Bonn, Bonn, Germany

    Thomas Schultz

  2. Department of Biomedical Engineering (IMT), Linköping University, Linköping, Sweden

    Evren Özarslan

  3. Department of Science & Technology, Linköping University, Norrköping, Sweden

    Ingrid Hotz

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Monfort, M., Luciani, T., Komperda, J., Ziebart, B., Mashayek, F., Marai, G.E. (2017). A Deep Learning Approach to Identifying Shock Locations in Turbulent Combustion Tensor Fields. In: Schultz, T., Özarslan, E., Hotz, I. (eds) Modeling, Analysis, and Visualization of Anisotropy. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-319-61358-1_16

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