About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
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Symposium
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AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
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Presentation Title |
Adaptive Latent Space Embedding for Real-Time 3D Diffraction Data Analysis |
Author(s) |
Alexander Scheinker, Reeju Pokharel |
On-Site Speaker (Planned) |
Alexander Scheinker |
Abstract Scope |
In-situ 3D characterization of defects and interfaces and their evolution at the mesoscale (nm-μm) are required to develop microstructure-aware physics-based models and to design advanced materials with tailored properties. There are two major challenges faced by the most advanced 3D imaging techniques. The first challenge is the long measurement time (0.5-8 hours) limiting the number of samples that can be imaged during a given beam time at a light source and also limits the temporal resolution of the dynamics being studied to quasi-static measurements. The second challenge is the long reconstruction time (days-weeks on HPS clusters) which makes it impossible to provide real-time feedback based on reconstructions during an ongoing experiment. Our work studies the use of adaptive tuning of a low-dimensional latent space embedding within 3D convolutional neural networks to speed up measurements (by reducing the number of measurements required) and reconstructions of 3D coherent diffraction imaging (CDI) methods. |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, Characterization, Computational Materials Science & Engineering |