Digitalizing metallic materials from image segmentation to multiscale solutions via physics informed operator learning
Abstract Fast prediction of microstructural responses based on realistic material topology is vital for linking process, structure, and properties. This work presents a digital framework for metallic materials using microscale features. We explore deep learning for two primary goals: (1) segmenting...
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| Автори: | , , , , , , , , , , , , , |
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| Формат: | Artigo |
| Мова: | Inglês |
| Опубліковано: |
Nature Portfolio
2025-08-01
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| Серія: | npj Computational Materials |
| Онлайн доступ: | https://doi.org/10.1038/s41524-025-01718-y |
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