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Machine Learning Applied Composite (33 results)
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Machine Learning Applied to Composite Materials
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Paperback. Condition: Brand New. 206 pages. 9.21x6.14x0.43 inches. In Stock.
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Machine Learning Applied to Composite Materials (Composites Science and Technology)
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Machine Learning Applied to Composite Materials (Composites Science and Technology)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Taschenbuch. Condition: 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.
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Condition: New. 1st ed. 2022 edition NO-PA16APR2015-KAP.
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Taschenbuch. Condition: 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.
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Buch. Condition: 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.
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Paperback. Condition: Brand New. 204 pages. 9.25x6.10x0.46 inches. In Stock.
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Hardcover. Condition: Brand New. 204 pages. 9.25x6.10x0.71 inches. In Stock.
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Machine Learning Aided Analysis, Design, and Additive Manufacturing of Functionally Graded Porous Composite Structures: Applied Methods in Mechanical ... Series in Composites Science and Engineering)
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Machine Learning Aided Analysis, Design, and Additive Manufacturing of Functionally Graded Porous Composite Structures : Applied Methods in Mechanical Performance Evaluation, Machine Learning Aided Analysis, and Additive Manufacturing
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Machine Learning Aided Analysis, Design, and Additive Manufacturing of Functionally Graded Porous Composite Structures : Applied Methods in Mechanical Performance Evaluation, Machine Learning Aided Analysis, and Additive Manufacturing
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Condition: new. Questo è un articolo print on demand.
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Condition: new. Questo è un articolo print on demand.
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Machine Learning Applied to Composite Materials
Language: English
Published by Springer Nature Singapore Dez 2023, 2023
ISBN 10: 9811962804 ISBN 13: 9789811962806
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 204 pp. Englisch.
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Machine Learning Applied to Composite Materials
Language: English
Published by Springer, Berlin|Springer Nature Singapore|Springer, 2023
ISBN 10: 9811962804 ISBN 13: 9789811962806
Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 n.
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Machine Learning Applied to Composite Materials
Language: English
Published by Springer, Berlin|Springer Nature Singapore|Springer, 2022
ISBN 10: 9811962774 ISBN 13: 9789811962776
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 n.
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Machine Learning Applied to Composite Materials
Language: English
Published by Springer Nature Singapore, Springer Nature Singapore Nov 2022, 2022
ISBN 10: 9811962774 ISBN 13: 9789811962776
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 204 pp. Englisch.
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Paperback. Condition: Brand New. 600 pages. 9.00x6.00x1.09 inches. In Stock. This item is printed on demand.
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Machine Learning Applied to Composite Materials
Language: English
Published by Springer, Springer Dez 2023, 2023
ISBN 10: 9811962804 ISBN 13: 9789811962806
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 204 pp. Englisch.
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Machine Learning Applied to Composite Materials
Language: English
Published by Springer, Springer Nov 2022, 2022
ISBN 10: 9811962774 ISBN 13: 9789811962776
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Buch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 204 pp. Englisch.
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Condition: New. Print on Demand.
