Super-resolving material microstructure image via deep learning for microstructure characterization and mechanical behavior analysis
Abstract The digitized format of microstructures, or digital microstructures, plays a crucial role in modern-day materials research. Unfortunately, the acquisition of digital microstructures through experimental means can be unsuccessful in delivering sufficient resolution that is necessary to captu...
Guardado en:
Autores principales: | Jaimyun Jung, Juwon Na, Hyung Keun Park, Jeong Min Park, Gyuwon Kim, Seungchul Lee, Hyoung Seop Kim |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/6bbe639d1e2f45d6a9cfc4f5f009486b |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Continuum understanding of twin formation near grain boundaries of FCC metals with low stacking fault energy
por: Jaimyun Jung, et al.
Publicado: (2017) -
Phase field modeling for the morphological and microstructural evolution of metallic materials under environmental attack
por: Talha Qasim Ansari, et al.
Publicado: (2021) -
Mechanical behavior predictions of additively manufactured microstructures using functional Gaussian process surrogates
por: Robert Saunders, et al.
Publicado: (2021) -
A review: applications of the phase field method in predicting microstructure and property evolution of irradiated nuclear materials
por: Yulan Li, et al.
Publicado: (2017) -
Accelerating phase-field-based microstructure evolution predictions via surrogate models trained by machine learning methods
por: David Montes de Oca Zapiain, et al.
Publicado: (2021)