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Meeting 2026 TMS Annual Meeting & Exhibition
Symposium Verification, Calibration, and Validation Approaches in Modeling the Mechanical Performance of Metallic Materials
Presentation Title Microstructure Evolution via Latent Transformers for 3D Materials Simulations and Characterization
Author(s) Reeju Pokharel, Alexander Scheinker
On-Site Speaker (Planned) Reeju Pokharel
Abstract Scope Advanced characterization of material microstructures during in situ experiments is crucial, yet real-time analysis of 3D multiscale data remains challenging. This work presents novel machine learning approaches enabling real-time microstructure characterization during experiments. We demonstrate two key advances: a deep learning framework accelerating diffraction data reconstruction with adaptive feedback mechanisms, and physics-informed surrogate models for rapid crystal plasticity prediction. Our approach combines a variational autoencoder with a transformer model to evolve microstructure states through strain increments, achieving orders-of-magnitude speedup compared to conventional methods while maintaining accuracy. This enables simulation of otherwise computationally prohibitive large volumes. The integration of physical principles improves model generalizability and transferability. Our unique contribution combines accelerated reconstruction with predictive simulations to enable simultaneous multiscale characterization with continuous model-guided feedback, allowing more efficient and informative in situ experiments at advanced light sources.
Proceedings Inclusion? Planned:
Keywords Machine Learning, Characterization, Mechanical Properties

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