DATA: Machine Learning for High Dimentional Data: Microstructure, Images, and Fields

FOCUS · C07 · ID: 3364052

Presentations

  • ORAL · Invited

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    Publication: Jekel, C. F., Sterbentz, D. M., Stitt, T. M., Mocz, P., Rieben, R. N., White, D. A., & Belof, J. L. (2024). Machine learning visualization tool for exploring parameterized hydrodynamics. Machine Learning: Science and Technology, 5(4), 045048.

    Presenters

    • Charles F Jekel

      • Lawrence Livermore National Laboratory

    Authors

    • Charles F Jekel

      • Lawrence Livermore National Laboratory

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  • ORAL

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    Presenters

    • David Oca Montes de Oca Zapiain

      • Sandia National Laboratories

    Authors

    • David Oca Montes de Oca Zapiain

      • Sandia National Laboratories

    • Samantha Brozak

      • Sandia National Laboratories

    • Brendan Donohoe

      • Sandia National Laboratories

    • Tommy Ao

      • Sandia National Laboratories

    • Mark Rodriguez

      • Sandia National Laboratories

    • Marcus David Knudson

      • Sandia National Laboratories

    • Nathan P Brown

      • Sandia National Laboratories

    • J Matthew D Lane

      • Sandia National Laboratories

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  • ORAL

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    Publication: 1. P. C. Nguyen, et al., PARCv2: Physics-aware Recurrent Convolutional Neural Networks for Spatiotemporal Dynamics Modeling, in Forty-first International Conference on Machine Learning (2024)
    2. X. Cheng, et al., Physics-aware recurrent convolutional neural networks for modeling multiphase compressible flows. International Journal of Multiphase Flow p. 104877 (2024)
    3. X. Cheng et al., A Physics-aware Deep Learning Model for Energetic Material Shear Band Formation in Weak Shock Regime, in preparation

    Presenters

    • Xinlun Cheng

      • University of Virginia

    Authors

    • Xinlun Cheng

      • University of Virginia

    • Yen t Nguyen

      • University of Iowa

    • Joseph Choi

      • University of Virginia

    • Pradeep Kumar Seshadri

      • University of Iowa

    • Mayank Verma

      • University of Iowa

    • H.S. Udaykumar

      • University of Iowa

    • Stephen Baek

      • University of Virginia

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  • ORAL

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    Publication: 1. "Topological analysis of X-ray CT data for the recognition and trending of subtle changes in microstructure under material aging," A. Maiti, A. Venkat, G. D. Kosiba, W. L. Shaw, J. D. Sain, R. K. Lindsey, C. D. Grant, P.-T. Bremer, A. G. Gyulassi, V. Pascucci, and R. H. Gee, Comput. Mat. Sci. 182, 109782 (2020).
    2. "Effect of thermal conditioning on the initiation threshold of secondary high explosives," A. Maiti, W. L. Shaw, S. M. Clarke, C. Fox, L. A. Ke, W. N. Cheung, M. A. Burton, G. D. Kosiba, C. D. Grant, R. H. Gee, Propell. Explos. Pyrot. 49(2), e202300253 (2024).
    3. "Image Distinguishability Analysis Testing through Principal Components and its Application to Hot Spot Scale Invariance," M. P. Kroonblawd, A. Maiti, and L. E. Fried, to be submitted (2025).
    4. "Classifying material microstructure of accelerated aged high explosives with a computer vision approach," G. D. Kosiba, A. Maiti, R. K. Lindsey, W. L Shaw, C. D. Grant, and R. H. Gee, to be submitted (2025).

    Presenters

    • Amitesh Maiti

      • Lawrence Livermore National Laboratory

    Authors

    • Amitesh Maiti

      • Lawrence Livermore National Laboratory

    • Graham D Kosiba

      • Lawrence Livermore National Laboratory

    • Matthew P Kroonblawd

      • Lawrence Livermore National Laboratory

    • Richard H Gee

      • Lawrence Livermore National Lab

    View abstract →