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Predicting material microstructure evolution via data-driven machine learning
Predicting microstructure evolution can be a formidable challenge, yet it is essential to building microstructure-processing-property relationships. Yang et al. offer a new solution to traditional partial differential equation-based simulations: a data-driven machine learning approach motivated by t...
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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Elsevier
2021
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8276005/ https://www.ncbi.nlm.nih.gov/pubmed/34286300 http://dx.doi.org/10.1016/j.patter.2021.100285 |
| Sumario: | Predicting microstructure evolution can be a formidable challenge, yet it is essential to building microstructure-processing-property relationships. Yang et al. offer a new solution to traditional partial differential equation-based simulations: a data-driven machine learning approach motivated by the practical needs to accelerate the materials design process and deal with incomplete information in the real world of microstructure simulation. |
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