Accelerating phase-field-based microstructure evolution predictions via surrogate models trained by machine learning methods
Abstract The phase-field method is a powerful and versatile computational approach for modeling the evolution of microstructures and associated properties for a wide variety of physical, chemical, and biological systems. However, existing high-fidelity phase-field models are inherently computational...
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| Main Authors: | , , |
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| Format: | article |
| Language: | EN |
| Published: |
Nature Portfolio
2021
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| Subjects: | |
| Online Access: | https://doaj.org/article/5364a3d9c8394b509924926f4946716f |
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