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Meeting

2026 TMS Annual Meeting & Exhibition

Symposium

AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling

Presentation Title

Physics-Informed Neural Networks for Parameter Quantification in Materials Science

Author(s)

Asfandyar Khan, Mahmood Mamivand

On-Site Speaker (Planned)

Asfandyar Khan

Abstract Scope

Accurate estimation of material parameters, such as gradient energy coefficients, is critical for modeling microstructural evolution but remains challenging due to experimental limitations. Our work focuses on Physics-Informed Neural Network (PINN) framework that integrates governing partial differential equations directly into the training process of a neural network, to enable simultaneous forward modeling and inverse parameter estimation from morphological data. We demonstrate the PINNs capability by recovering the gradient energy coefficient from an observed microstructure evolution data across varying spatial resolutions. The inverse model consistently estimates physically meaningful parameters, even from coarse data, highlighting the robustness and generalization ability of PINNs. This methodology offers a scalable and data-efficient approach for solving inverse problems in materials science and can be extended to a wide range of systems where direct measurements are impractical.

Proceedings Inclusion?

Planned:

Keywords

Modeling and Simulation, Computational Materials Science & Engineering, Phase Transformations

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