Self-supervised learning and prediction of microstructure evolution with convolutional recurrent neural networks

Microstructural evolution is a key aspect of understanding and exploiting the processing-structure-property relationship of materials. Modeling microstructure evolution usually relies on coarse-grained simulations with evolution principles described by partial differential equations (PDEs). Here we...

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Detalles Bibliográficos
Autores principales: Yang, Kaiqi, Cao, Yifan, Zhang, Youtian, Fan, Shaoxun, Tang, Ming, Aberg, Daniel, Sadigh, Babak, Zhou, Fei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8134942/
https://www.ncbi.nlm.nih.gov/pubmed/34036288
http://dx.doi.org/10.1016/j.patter.2021.100243

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8134942/
https://www.ncbi.nlm.nih.gov/pubmed/34036288
http://dx.doi.org/10.1016/j.patter.2021.100243

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