Physics-Informed Attention Mechanism and Generalization Capability of Deep Learning-Based Grain Growth Evolution Prediction
arXiv:2606.17235v1 Announce Type: cross Abstract: Machine Learning (ML) models for grain growth prediction are typically trained on idealized synthetic data, yet practical applications require generalization to conditions outside the training distribution. This study evaluated the Out-Of-Distribution (OOD) generalization capability of the trained model from our previous study across three test cases, including experimental microstructures, microstructures characterized by a bimodal grain size distribution, and abnormal grain growth. To further probe whether physics-informed architectural desig
The increasing sophistication of AI models and the critical need for their robust performance in real-world material science applications necessitate research into generalization capabilities.
Improving AI generalization in materials science can accelerate the discovery and optimization of new materials, impacting industries from energy to manufacturing.
The ability of deep learning models to reliably predict material behavior under diverse, unseen conditions moves closer to practical implementation, reducing experimental trial-and-error.
- · Materials science research institutions
- · Advanced manufacturing sector
- · AI/ML model developers
- · Physics-informed AI community
- · Traditional, purely empirical materials discovery methods
More efficient material design and development processes become achievable through AI.
Accelerated innovation in areas reliant on novel materials, such as battery technology or aerospace components.
Reduced costs and time-to-market for complex material systems, leading to competitive advantages for nations investing in this research.
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Read at arXiv cs.AI