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Titlebook: Representation Surfaces for Physical Properties of Materials; A Visual Approach to Manuel Laso,Nieves Jimeno Textbook 2020 Springer Nature

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書(shū)目名稱Representation Surfaces for Physical Properties of Materials
副標(biāo)題A Visual Approach to
編輯Manuel Laso,Nieves Jimeno
視頻videohttp://file.papertrans.cn/828/827396/827396.mp4
概述The first book entirely devoted to representation surfaces and their applications.Includes worked-out examples, problems and exercises.Provides the fundamentals of materials science, crystallography a
叢書(shū)名稱Engineering Materials
圖書(shū)封面Titlebook: Representation Surfaces for Physical Properties of Materials; A Visual Approach to Manuel Laso,Nieves Jimeno Textbook 2020 Springer Nature
描述.This textbook presents all the mathematical and physical concepts needed to visualize and understand representation surfaces, providing readers with a reliable and intuitive understanding of the behavior and properties of anisotropic materials, and a sound grasp of the directionality of material properties. They will learn how to extract quantitative information from representation surfaces, which encode tremendous amounts of information in a very concise way, making them especially useful in understanding higher order tensorial material properties (piezoelectric moduli, elastic compliance and rigidity, etc.) and in the design of applications based on these materials. Readers will also learn from scratch concepts on crystallography, symmetry and Cartesian tensors, which are essential for understanding anisotropic materials, their design and application. The book describes how to apply representation surfaces to a diverse range of material properties, making it a valuable resource for material scientists, mechanical engineers, and solid state physicists, as well as advanced undergraduates in Materials Science, Solid State Physics, Electronics, Optics, Mechanical Engineering, Compos
出版日期Textbook 2020
關(guān)鍵詞Anisotropic Materials textbook; Textured Materials; Crystalline Materials; Smart Materials; Cartesian Te
版次1
doihttps://doi.org/10.1007/978-3-030-40870-1
isbn_softcover978-3-030-40872-5
isbn_ebook978-3-030-40870-1Series ISSN 1612-1317 Series E-ISSN 1868-1212
issn_series 1612-1317
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

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Manuel Laso,Nieves Jimenoerall performance of FSO systems. This chapter offers an overview of how these machine learning models could be utilized in future generation wireless communication systems. The chapter discusses the various existing contributions for the implementation of machine learning algorithms in the field of
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Manuel Laso,Nieves Jimenooise ratio (SNR) conditions to ascertain the coding gain and diversity order afforded by the system. These asymptotic findings inform a power optimization scheme and offering formulas for optimal power transmission at the transmitting nodes. Moreover, an approximate assessment of the ergodic capacit
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Representation of Material Properties by Means of Cartesian Tensors, and “contravariant” are not necessary. The manipulation rules for Cartesian tensors are but a minor extension of the familiar rules for vector and matrices. In the rest of the book the adjective “Cartesian” will often be dropped for brevity.
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1612-1317 des the fundamentals of materials science, crystallography a.This textbook presents all the mathematical and physical concepts needed to visualize and understand representation surfaces, providing readers with a reliable and intuitive understanding of the behavior and properties of anisotropic mater
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