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Meeting 2027 TMS Annual Meeting & Exhibition
Symposium Algorithms Development in Materials Science and Engineering
Presentation Title Simulation-Informed Deep Learning for Morphology-Based Parameter Estimation in Fe–Cr Alloys
Author(s) Asfandyar Khan, Mahmood Mamivand
On-Site Speaker (Planned) Asfandyar Khan
Abstract Scope Phase-field simulations are widely used to study microstructure evolution, but reliable predictions depend on parameters such as gradient energy coefficients and mobilities that are often calibrated indirectly. This work presents a simulation-informed deep-learning approach for estimating phase-field parameters from microstructural morphology in Fe–Cr spinodal decomposition. Synthetic concentration maps are generated with phase field simulations over a controlled parameter space, and a convolutional neural network is trained to map microstructure images directly to the governing gradient-energy and mobility parameters. The model is evaluated on unseen simulated data, off-grid parameter combinations, and morphology regimes where different parameter sets can produce similar coarsened patterns. Results show that image-based learning can provide rapid parameter estimates when morphology remains sensitive to the underlying physics. This approach offers a computationally efficient route for repeated microstructure-based calibration and supports future integration of experimental characterization with phase-field modeling.
Proceedings Inclusion? Planned:
Keywords Computational Materials Science & Engineering, Machine Learning, Modeling and Simulation

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