Materials Discovery and Design By Means of Data Science and Optimal Learning /

• Corporate Author: SpringerLink (Online service)
• Other Authors: Lookman, Turab (Editor, http://id.loc.gov/vocabulary/relators/edt), Eidenbenz, Stephan (Editor, http://id.loc.gov/vocabulary/relators/edt), Alexander, Frank (Editor, http://id.loc.gov/vocabulary/relators/edt), Barnes, Cris (Editor, http://id.loc.gov/vocabulary/relators/edt)
• Format: Electronic eBook
• Language: English
• Published: Cham : Springer International Publishing : Imprint: Springer, 2018.
• Edition: 1st ed. 2018.
• Series: Springer Series in Materials Science, 280
• Subjects: Physics.; Materials science.; Data mining.; Engineering-Materials.; Computer mathematics.; Numerical analysis.; Numerical and Computational Physics, Simulation.; Characterization and Evaluation of Materials.; Data Mining and Knowledge Discovery.; Materials Engineering.; Computational Science and Engineering.; Numerical Analysis.

This book addresses the current status, challenges and future directions of data-driven materials discovery and design. It presents the analysis and learning from data as a key theme in many science and cyber related applications. The challenging open questions as well as future directions in the ap...