Cargando…
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...
Autor principal: | Kautz, Elizabeth J. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
|
Materias: | |
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 |
Ejemplares similares
-
Data-centric approach to improve machine learning models for inorganic materials
por: Bartel, Christopher J.
Publicado: (2021) -
Preview of machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery
por: Callaghan, Sarah
Publicado: (2021) -
Practical machine learning for disease diagnosis
por: Summers, Huw D.
Publicado: (2021) -
Preview of “Data and its (dis)contents: A survey of dataset development and use in machine learning research”
por: Vincent, Nicholas, et al.
Publicado: (2021) -
Data-driven assessment of dimension reduction quality for single-cell omics data
por: Dong, Xiaoru, et al.
Publicado: (2022)