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Image-Based Simulation

Scott Roberts, Sandia National Laboratories

Joseph Bishop, Sandia National Laboratories

Karel Matous, University of Notre Dame

John Korbin, Sandia National Laboratories

Nagi Mansour, NASA

Advances in three-dimensional imaging techniques, including x-ray computed tomography and scanning electron microscopy, have enabled insight into as-manufactured materials and components like never before.  This abundance of image data has inspired scientists and engineers to perform simulations on computational domains derived directly from this image data, a foundational aspect of the increasingly popular digital twin concept.  However, the process converting greyscale three-dimensional image data to a discretized domain suitable for simulation is often arduous and fraught with errors.

In this minisymposium, we explore techniques for improving this image-to-simulation process. Topics of interest include, but are not limited to:
•    Computed tomography reconstruction techniques to reduce artifacts
•    Image segmentation, labeling, and part identification
•    Computer vision and machine learning approaches
•    Geometric feature identification and detection
•    Domain discretization / mesh generation
•    Data compression and model reduction
•    Algorithms and numerical methods for solving multi-physics problems on image data
•    High performance computing applied to image datasets
•    Applications of the above techniques to real-world scientific and engineering applications
•    Credibility assessments, including uncertainty quantification and validation