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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Detalles Bibliográficos
Autor principal: Kautz, Elizabeth J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
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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

Internet

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

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