Predicting Grain Growth in Polycrystalline Materials Using Deep Learning Time Series Models

• URL: http://arxiv.org/abs/2511.11630v1
• Date: Fri, 07 Nov 2025 18:29:42 GMT
• Title: Predicting Grain Growth in Polycrystalline Materials Using Deep Learning Time Series Models
• Authors: Eliane Younes, Elie Hachem, Marc Bernacki,
• Abstract summary: Grain Growth strongly influences the mechanical behavior of materials, making its prediction a key objective in microstructural engineering.<n>In this study, several deep learning approaches were evaluated, including recurrent neural networks (RNN), long short-term memory (LSTM), temporal convolutional networks (TCN), and transformers.<n>The LSTM network achieved the highest accuracy (above 90%) and the most stable performance, maintaining physically consistent predictions over extended horizons.
• Score: 0.9558392439655014
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Grain Growth strongly influences the mechanical behavior of materials, making its prediction a key objective in microstructural engineering. In this study, several deep learning approaches were evaluated, including recurrent neural networks (RNN), long short-term memory (LSTM), temporal convolutional networks (TCN), and transformers, to forecast grain size distributions during grain growth. Unlike full-field simulations, which are computationally demanding, the present work relies on mean-field statistical descriptors extracted from high-fidelity simulations. A dataset of 120 grain growth sequences was processed into normalized grain size distributions as a function of time. The models were trained to predict future distributions from a short temporal history using a recursive forecasting strategy. Among the tested models, the LSTM network achieved the highest accuracy (above 90\%) and the most stable performance, maintaining physically consistent predictions over extended horizons while reducing computation time from about 20 minutes per sequence to only a few seconds, whereas the other architectures tended to diverge when forecasting further in time. These results highlight the potential of low-dimensional descriptors and LSTM-based forecasting for efficient and accurate microstructure prediction, with direct implications for digital twin development and process optimization.

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