Predicting Grain Growth in Polycrystalline Materials Using Deep Learning Time Series Models
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866917080670928896 |
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| author | Younes, Eliane Hachem, Elie Bernacki, Marc |
| author_facet | Younes, Eliane Hachem, Elie Bernacki, Marc |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_11630 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Predicting Grain Growth in Polycrystalline Materials Using Deep Learning Time Series Models Younes, Eliane Hachem, Elie Bernacki, Marc Machine Learning Materials Science Artificial Intelligence Computational Engineering, Finance, and Science 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. |
| title | Predicting Grain Growth in Polycrystalline Materials Using Deep Learning Time Series Models |
| topic | Machine Learning Materials Science Artificial Intelligence Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2511.11630 |