Materials Discovery and Design: By Means of Data Science and Optimal Learning - Couverture rigide
Livre 188 sur 233: Springer Series in Materials Science
Synopsis
Develops a new paradigm and way of thinking how data science can guide materials discoveries
Describes information-theoretic tools and their application to materials science
Covers both analysis and processing of large scale computational and experimental data in materials science
With contributions from an interdisciplinary group of experts in the field
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Materials Discovery and Design: By Means of Data Science and Optimal Learning (Springer Series in Materials Science, 280, Band 280) [Hardcover] Lookman, Turab; Eidenbenz, Stephan; Alexander, Frank and Barnes, Cris
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Springer, 2018
ISBN 10 : 3319994646
ISBN 13 : 9783319994642
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Vendeur : SpringBooks, Berlin, Allemagne
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Hardcover. Etat : As New. 1. Auflage. from Germany, will be dispatched immediately. N° de réf. du vendeur CE-2403C-BLAESSHUHN-01-1000XS
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Materials Discovery and Design (eng)
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Etat : new. Questo è un articolo print on demand. N° de réf. du vendeur 7UN2AS1HMB
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Materials Discovery and Design: By Means of Data Science and Optimal Learning (Springer Series in Materials Science, 280)
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Materials Discovery and Design
Edité par
Springer International Publishing, 2018
ISBN 10 : 3319994646
ISBN 13 : 9783319994642
Neuf
Couverture rigide
Vendeur : moluna, Greven, Allemagne
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Gebunden. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Develops a new paradigm for using data science to guide materials discoveriesDescribes information-theoretic tools and their application to materials scienceCovers both analysis and processing of large scale computational and experiment. N° de réf. du vendeur 235194413
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Materials Discovery and Design
Edité par
Springer International Publishing Okt 2018, 2018
ISBN 10 : 3319994646
ISBN 13 : 9783319994642
Neuf
Couverture rigide
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 application of data science to materials problems are sketched. Computational and experimental facilities today generate vast amounts of data at an unprecedented rate. The book gives guidance to discover new knowledge that enables materials innovation to address grand challenges in energy, environment and security, the clearer link needed between the data from these facilities and the theory and underlying science. The role ofinference and optimization methods in distilling the data and constraining predictions using insights and results from theory is key toachieving the desired goals of real time analysis and feedback.Thus, the importance of this book lies in emphasizing that the full value of knowledge driven discovery using data can only be realized by integrating statistical and information sciences with materials science, which is increasingly dependent on high throughput and large scale computational and experimental data gathering efforts. This is especially the case as we enter a new era of big data in materials science with the planning of future experimental facilities such as the Linac Coherent Light Source at Stanford (LCLS-II), the European X-ray Free Electron Laser (EXFEL) and MaRIE (Matter Radiation in Extremes), the signature concept facility from Los Alamos National Laboratory. These facilities are expected to generate hundreds of terabytes to several petabytes ofin situspatially and temporally resolved data per sample.The questions that then arise include how we can learn from the data to accelerate the processing and analysis of reconstructed microstructure, rapidly map spatially resolved properties from high throughput data, devise diagnostics for pattern detection, and guide experiments towards desired targeted properties. The authors are an interdisciplinary group of leading experts who bring the excitement of the nascent and rapidly emerging field of materials informatics to the reader. 272 pp. Englisch. N° de réf. du vendeur 9783319994642
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Materials Discovery and Design: By Means of Data Science and Optimal Learning: 280 (Springer Series in Materials Science)
Vendeur : Majestic Books, Hounslow, Royaume-Uni
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Materials Discovery and Design: By Means of Data Science and Optimal Learning: 280 (Springer Series in Materials Science)
Vendeur : Books Puddle, New York, NY, Etats-Unis
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Etat : New. 1st ed. 2018 edition NO-PA16APR2015-KAP. N° de réf. du vendeur 26376477756
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Materials Discovery and Design
ISBN 10 : 3319994646
ISBN 13 : 9783319994642
Neuf
Couverture rigide
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 application of data science to materials problems are sketched. Computational and experimental facilities today generate vast amounts of data at an unprecedented rate. The book gives guidance to discover new knowledge that enables materials innovation to address grand challenges in energy, environment and security, the clearer link needed between the data from these facilities and the theory and underlying science. The role of inference and optimization methods in distilling the data and constraining predictions using insights and results from theory is key to achieving the desired goals of real time analysis and feedback. Thus, the importance of this book lies in emphasizing that the full value of knowledge driven discovery using data can only be realized by integrating statistical and information sciences with materials science, which is increasingly dependent on high throughput and large scale computational and experimental data gathering efforts. This is especially the case as we enter a new era of big data in materials science with the planning of future experimental facilities such as the Linac Coherent Light Source at Stanford (LCLS-II), the European X-ray Free Electron Laser (EXFEL) and MaRIE (Matter Radiation in Extremes), the signature concept facility from Los Alamos National Laboratory. These facilities are expected to generate hundreds of terabytes to several petabytes of in situ spatially and temporally resolved data per sample. The questions that then arise include how we can learn from the data to accelerate the processing and analysis of reconstructed microstructure, rapidly map spatially resolved properties from high throughput data, devise diagnostics for pattern detection, and guide experiments towards desired targeted properties. The authors are an interdisciplinary group of leading experts who bring the excitement of the nascent and rapidly emerging field of materials informatics to the reader.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 272 pp. Englisch. N° de réf. du vendeur 9783319994642
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Materials Discovery and Design: By Means of Data Science and Optimal Learning: 280 (Springer Series in Materials Science)
impression à la demandeVendeur : Biblios, Frankfurt am main, HESSE, Allemagne
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Materials Discovery and Design : By Means of Data Science and Optimal Learning
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 application of data science to materials problems are sketched. Computational and experimental facilities today generate vast amounts of data at an unprecedented rate. The book gives guidance to discover new knowledge that enables materials innovation to address grand challenges in energy, environment and security, the clearer link needed between the data from these facilities and the theory and underlying science. The role ofinference and optimization methods in distilling the data and constraining predictions using insights and results from theory is key toachieving the desired goals of real time analysis and feedback.Thus, the importance of this book lies in emphasizing that the full value of knowledge driven discovery using data can only be realized by integrating statistical and information sciences with materials science, which is increasingly dependent on high throughput and large scale computational and experimental data gathering efforts. This is especially the case as we enter a new era of big data in materials science with the planning of future experimental facilities such as the Linac Coherent Light Source at Stanford (LCLS-II), the European X-ray Free Electron Laser (EXFEL) and MaRIE (Matter Radiation in Extremes), the signature concept facility from Los Alamos National Laboratory. These facilities are expected to generate hundreds of terabytes to several petabytes ofin situspatially and temporally resolved data per sample.The questions that then arise include how we can learn from the data to accelerate the processing and analysis of reconstructed microstructure, rapidly map spatially resolved properties from high throughput data, devise diagnostics for pattern detection, and guide experiments towards desired targeted properties. The authors are an interdisciplinary group of leading experts who bring the excitement of the nascent and rapidly emerging field of materials informatics to the reader. N° de réf. du vendeur 9783319994642
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