This course aims to provide a succinct overview of the emerging discipline of Materials Informatics at the intersection of materials science, computational science, and information science. Attention is drawn to specific opportunities afforded by this new field in accelerating materials development and deployment efforts. A particular emphasis is placed on materials exhibiting hierarchical internal structures spanning multiple length/structure scales and the impediments involved in establishing invertible process-structure-property (PSP) linkages for these materials. More specifically, it is argued that modern data sciences (including advanced statistics, dimensionality reduction, and formulation of metamodels) and innovative cyberinfrastructure tools (including integration platforms, databases, and customized tools for enhancement of collaborations among cross-disciplinary team members) are likely to play a critical and pivotal role in addressing the above challenges.
Ofrecido Por
Materials Data Sciences and Informatics
Instituto de Tecnología de GeorgiaAcerca de este Curso
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Prueba Coursera para negociosHabilidades que obtendrás
- Informatics
- Materials
- Statistics
- Data Science
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Prueba Coursera para negocios
Ofrecido por
Programa - Qué aprenderás en este curso
Welcome
Accelerating Materials Development and Deployment
Materials Knowledge and Materials Data Science
Materials Knowledge Improvement Cycles
Case Study in Homogenization: Plastic Properties of Two-Phase Composites
Reseñas
- 5 stars62,90 %
- 4 stars26,45 %
- 3 stars7,09 %
- 2 stars2,25 %
- 1 star1,29 %
Principales reseñas sobre MATERIALS DATA SCIENCES AND INFORMATICS
Excellent experience. It engages my knowledge broadly about Data Science in Materials. Thank you
Good theory lessons. There should have been more focus on utilising software (PyMKS) to implement concepts, throughout the course rather than just the end
Thank you, Teacher!! It is amazing course and I improve my knowledge and skills in this field!!
Interesting content! Liked the explanation of principle component analysis.
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