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Meeting 2025 TMS Annual Meeting & Exhibition
Symposium AI/Data Informatics: Computational Model Development, Verification, Validation, and Uncertainty Quantification
Presentation Title Adaptive Latent Space Tuning to Enable Characterizing Materials Dynamics Using Bragg Coherent Diffraction Imaging
Author(s) Alexander Scheinker, Reeju Pokharel
On-Site Speaker (Planned) Alexander Scheinker
Abstract Scope Bragg coherent diffraction imaging (BCDI) enables detailed measurements of 3D grain evolution in polycrystalline samples under in situ thermo-mechanical loading. However, BCDI's raw intensity measurements lack phase information, necessitating lengthy iterative phase-retrieval that can produce inconsistent solutions. We have developed a deep convolutional neural network approach for uniquely tracking a 3D grain's electron density under in situ conditions using a physics-informed adaptive machine learning algorithm. Our novel method represents high-dimensional 3D diffraction data as low-dimensional latent space embedding, which can be adaptively traversed to account for time-varying distribution shift in real-time. We prove the convergence properties of our mechanisms to the unique optimal solution for CDI problems across a wide function class. This approach overcomes BCDI's main challenge of non-unique reconstructions and offers a computationally efficient solution for real-time 3D grain tracking in complex materials under dynamic conditions.
Proceedings Inclusion? Planned:
Keywords ICME, Characterization, Machine Learning

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