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Data analytics has become an integral part of materials science. This book provides the practical tools and fundamentals needed for researchers in materials science to understand how to analyze large datasets using statistical methods, especially inverse methods applied to microstructure characterization. It contains valuable guidance on essential topics such as denoising and data modeling. Additionally, the analysis and applications section addresses compressed sensing methods, stochastic models, extreme estimation, and approaches to pattern detection.
Acerca del autor: Jeffrey P. Simmons, Lawrence F. Drummy, Charles A. Bouman, Marc De Graef
Título: Statistical Methods for Materials Science
Editorial: CRC Press
Año de publicación: 2019
Encuadernación: Encuadernación de tapa dura
Condición: Brand New
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Gebunden. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Jeffrey P. Simmons is a Scientist with the Materials and Manufacturing Directorate of the Air Force Research Laboratory (AFRL). He received the B.S. degree in metallurgical engineering from the New Mexico Institute of Mining and Technolo. Nº de ref. del artículo: 596130600
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Buch. Condición: Neu. Statistical Methods for Materials Science | The Data Science of Microstructure Characterization | Wright-Patterson Air Force Base, Ohio, USA) Jeffrey P. Simmons (Air Force Research Laboratory (u. a.) | Buch | Einband - fest (Hardcover) | Englisch | 2019 | CRC Press | EAN 9781498738200 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand. Nº de ref. del artículo: 133244674
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Hardcover. Condición: new. Hardcover. Data analytics has become an integral part of materials science. This book provides the practical tools and fundamentals needed for researchers in materials science to understand how to analyze large datasets using statistical methods, especially inverse methods applied to microstructure characterization. It contains valuable guidance on essential topics such as denoising and data modeling. Additionally, the analysis and applications section addresses compressed sensing methods, stochastic models, extreme estimation, and approaches to pattern detection. This book provides the practical tools and fundamentals needed for researchers in materials science to understand how to analyze large datasets using statistical methods, especially inverse methods applied to microstructure characterization. It contains valuable guidance on essential topics such as denoising and data modeling. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9781498738200
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Hardcover. Condición: new. Hardcover. Data analytics has become an integral part of materials science. This book provides the practical tools and fundamentals needed for researchers in materials science to understand how to analyze large datasets using statistical methods, especially inverse methods applied to microstructure characterization. It contains valuable guidance on essential topics such as denoising and data modeling. Additionally, the analysis and applications section addresses compressed sensing methods, stochastic models, extreme estimation, and approaches to pattern detection. This book provides the practical tools and fundamentals needed for researchers in materials science to understand how to analyze large datasets using statistical methods, especially inverse methods applied to microstructure characterization. It contains valuable guidance on essential topics such as denoising and data modeling. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Nº de ref. del artículo: 9781498738200
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