kushvaha vinod (18 Ergebnisse)

Zustand: New. In.

Zustand: New. In.

Paperback. Zustand: Brand New. 206 pages. 9.21x6.14x0.43 inches. In Stock.

Taschenbuch. Zustand: Neu. Fatigue assessment of composite laminates | A computational approach to assess the fatigue | Vinod Kushvaha | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2010 | VDM Verlag Dr. Müller | EAN 9783639312461 | Verantwortliche Person für die EU: OmniScriptum GmbH & Co. KG, Bahnhofstr. 28, 66111 Saarbrücken, info[at]akademikerverlag[dot]de | Anbieter: preigu.

Zustand: New.

Paperback. Zustand: Brand New. 350 pages. 9.00x6.00x0.63 inches. In Stock.

Zustand: Hervorragend. Zustand: Hervorragend | Seiten: 312 | Sprache: Englisch | Produktart: Bücher | This book presents recent advancements of machine learning methods and their applications in material science and nanotechnologies. It provides an introduction to the field and for those who wish to explore machine learning in modeling as well as conduct data analyses of material characteristics. The book discusses ways to enhance the material¿s electrical and mechanical properties based on available regression methods for supervised learning and optimization of material attributes. In summary, the growing interest among academics and professionals in the field of machine learning methods in functional nanomaterials such as sensors, solar cells, and photocatalysis is the driving force for behind this book. This is a comprehensive scientific reference book on machine learning for advanced functional materials and provides an in-depth examination of recent achievements in material science by focusing on topical issues using machine learning methods.

Taschenbuch. Zustand: Neu. Machine Learning for Advanced Functional Materials | Nirav Joshi (u. a.) | Taschenbuch | viii | Englisch | 2024 | Springer | EAN 9789819903955 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Zustand: New. In.

Zustand: New. In.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents recent advancements of machine learning methods and their applications in material science and nanotechnologies. It provides an introduction to the field and for those who wish to explore machine learning in modeling as well as conduct data analyses of material characteristics. The book discusses ways to enhance the material's electrical and mechanical properties based on available regression methods for supervised learning and optimization of material attributes. In summary, the growing interest among academics and professionals in the field of machine learning methods in functional nanomaterials such as sensors, solar cells, and photocatalysis is the driving force for behind this book. This is a comprehensive scientific reference book on machine learning for advanced functional materials and provides an in-depth examination of recent achievements in material science by focusing on topical issues using machine learning methods.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents recent advancements of machine learning methods and their applications in material science and nanotechnologies. It provides an introduction to the field and for those who wish to explore machine learning in modeling as well as conduct data analyses of material characteristics. The book discusses ways to enhance the material's electrical and mechanical properties based on available regression methods for supervised learning and optimization of material attributes. In summary, the growing interest among academics and professionals in the field of machine learning methods in functional nanomaterials such as sensors, solar cells, and photocatalysis is the driving force for behind this book. This is a comprehensive scientific reference book on machine learning for advanced functional materials and provides an in-depth examination of recent achievements in material science by focusing on topical issues using machine learning methods.

Hardcover. Zustand: Brand New. 311 pages. 9.25x6.10x0.71 inches. In Stock.

Taschenbuch. Zustand: Neu. Machine Learning Applied to Composite Materials | Vinod Kushvaha (u. a.) | Taschenbuch | Composites Science and Technology | vi | Englisch | 2023 | Springer | EAN 9789811962806 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book introduces the approach of Machine Learning (ML) based predictive models in the design of composite materials to achieve the required properties for certain applications. ML can learn from existing experimental data obtained from very limited number of experiments and subsequently can be trained to find solutions of the complex non-linear, multi-dimensional functional relationships without any prior assumptions about their nature. In this case the ML models can learn from existing experimental data obtained from (1) composite design based on various properties of the matrix material and fillers/reinforcements (2) material processing during fabrication (3) property relationships. Modelling of these relationships using ML methods significantly reduce the experimental work involved in designing new composites, and therefore offer a new avenue for material design and properties. The book caters to students, academics and researchers who are interested in the field of materialcomposite modelling and design.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book introduces the approach of Machine Learning (ML) based predictive models in the design of composite materials to achieve the required properties for certain applications. ML can learn from existing experimental data obtained from very limited number of experiments and subsequently can be trained to find solutions of the complex non-linear, multi-dimensional functional relationships without any prior assumptions about their nature. In this case the ML models can learn from existing experimental data obtained from (1) composite design based on various properties of the matrix material and fillers/reinforcements (2) material processing during fabrication (3) property relationships. Modelling of these relationships using ML methods significantly reduce the experimental work involved in designing new composites, and therefore offer a new avenue for material design and properties. The book caters to students, academics and researchers who are interested in the field of materialcomposite modelling and design.

Paperback. Zustand: Brand New. 204 pages. 9.25x6.10x0.46 inches. In Stock.

Hardcover. Zustand: Brand New. 204 pages. 9.25x6.10x0.71 inches. In Stock.