Search Results for author: Jun-Sang Park

Found 3 papers, 2 papers with code

Rapid detection of rare events from in situ X-ray diffraction data using machine learning

no code implementations7 Dec 2023 Weijian Zheng, Jun-Sang Park, Peter Kenesei, Ahsan Ali, Zhengchun Liu, Ian T. Foster, Nicholas Schwarz, Rajkumar Kettimuthu, Antonino Miceli, Hemant Sharma

These methods are often combined with external stimuli such as thermo-mechanical loading to take snapshots over time of the evolving microstructure and attributes.

Representation Learning

BraggNN: Fast X-ray Bragg Peak Analysis Using Deep Learning

2 code implementations18 Aug 2020 Zhengchun Liu, Hemant Sharma, Jun-Sang Park, Peter Kenesei, Antonino Miceli, Jonathan Almer, Rajkumar Kettimuthu, Ian Foster

When applied to a real experimental dataset, a 3D reconstruction that used peak positions computed by BraggNN yields 15% better results on average as compared to a reconstruction obtained using peak positions determined using conventional 2D pseudo-Voigt fitting.

3D Reconstruction

High-energy coherent X-ray diffraction microscopy of polycrystal grains: first steps towards a multi-scale approach

1 code implementation28 Mar 2019 Siddharth Maddali, Jun-Sang Park, Hemant Sharma, Sarvjit Shastri, Peter Kenesei, Jonathan Almer, Ross Harder, Matthew J. Highland, Youssef S. G. Nashed, Stephan O. Hruszkewycz

We present proof-of-concept imaging measurements of a polycrystalline material that integrate the elements of conventional high-energy X-ray diffraction microscopy with coherent diffraction imaging techniques, and that can enable in-situ strain-sensitive imaging of lattice structure in ensembles of deeply embedded crystals over five decades of length scale upon full realization.

Materials Science Applied Physics

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