9786202196697 - data driven approach to the compositional design of metallic glasses de tripathi, manwendra kumar; ganguly, subhas (6 resultados)
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Librería: preigu, Osnabrück, Alemaniapreigu
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Taschenbuch. Condición: Neu. Data Driven Approach to the Compositional Design of Metallic Glasses | Manwendra Kumar Tripathi (u. a.) | Taschenbuch | 256 S. | Englisch | 2018 | LAP LAMBERT Academic Publishing | EAN 9786202196697 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[a…t]bod[dot]de | Anbieter: preigu.
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Librería: Revaluation Books, Exeter, Reino UnidoRevaluation Books
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Paperback. Condición: Brand New. 256 pages. 8.66x5.91x0.58 inches. In Stock.
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -There have been many compositional design methodologies developed for metallic glasses, which exhibit many attractive engineering properties, since past six decades. However, these methodologies have yielded marginal success and are… constrained by their own limitations. The data driven models, harnessing the benefits of machine learning, has been proven useful to solve complex engineering problems. The researcher in the domain of metallic glasses have produced enough experimental data to mine knowledge and build a reliable model to predict promising compositions. This book intends to address the need of those researchers, who have been in search of a case study of application of machine learning based techniques, for design of materials composition. The authors believe that this book will be useful to the researchers in the domain of Compositional Design of Metallic Glasses in particular, and Materials Engineers in general. 256 pp. Englisch.
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Librería: moluna, Greven, Alemaniamoluna
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Tripathi Manwendra KumarDr. Manwendra Tripathi has completed his PhD in Compositional Design of Bulk Metallic Glasses using Material Informatics from Indian Institute of Technology Shibpur, India. He ha…s nine years of teaching and re.
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000
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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -There have been many compositional design methodologies developed for metallic glasses, which exhibit many attractive engineering properties, since past six decades. However, these methodologies have yielded marginal success and are con…strained by their own limitations. The data driven models, harnessing the benefits of machine learning, has been proven useful to solve complex engineering problems. The researcher in the domain of metallic glasses have produced enough experimental data to mine knowledge and build a reliable model to predict promising compositions. This book intends to address the need of those researchers, who have been in search of a case study of application of machine learning based techniques, for design of materials composition. The authors believe that this book will be useful to the researchers in the domain of Compositional Design of Metallic Glasses in particular, and Materials Engineers in general.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 256 pp. Englisch.
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - There have been many compositional design methodologies developed for metallic glasses, which exhibit many attractive engineering properties, since past six decades. However, these methodologies have yielded marginal success and are cons…trained by their own limitations. The data driven models, harnessing the benefits of machine learning, has been proven useful to solve complex engineering problems. The researcher in the domain of metallic glasses have produced enough experimental data to mine knowledge and build a reliable model to predict promising compositions. This book intends to address the need of those researchers, who have been in search of a case study of application of machine learning based techniques, for design of materials composition. The authors believe that this book will be useful to the researchers in the domain of Compositional Design of Metallic Glasses in particular, and Materials Engineers in general.
