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.

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Dati bibliografici

Titolo

Statistical Methods for Materials Science (Hardcover)

Editore

Taylor & Francis Inc, Bosa Roca

Data di pubblicazione

2019

Lingua

Inglese

ISBN 10

1498738206

ISBN 13

9781498738200

Rilegatura

Hardcover

Condizione

new

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Riassunto

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.

Informazioni sull?autore

Jeffrey P. Simmons, Lawrence F. Drummy, Charles A. Bouman, Marc De Graef

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