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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| Autores principales: | , , |
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| Formato: | article |
| Lenguaje: | EN |
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Nature Portfolio
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/5364a3d9c8394b509924926f4946716f |
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