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Materials Data Science

E-BookPDF1 - PDF WatermarkE-Book
Englisch
Springer Nature Switzerlanderschienen am08.05.20242024
This text covers all of the artificial intelligence, deep learning, and data science topics relevant to materials science and engineering, accompanied by numerous examples and applications. The book begins with a concise introduction to statistics and probabilities, explaining important concepts and definitions such as probability functions and distributions, sampling and data preparation, Bayes' theorem, and statistical significance testing in the context of materials science. As such it is a useful introduction for both undergraduate and graduate students as well as a refresher for research scientists and practicing engineers. The second part is a detailed description of (statistical) machine learning and deep learning. It considers a range of supervised and unsupervised methods including multi-output regression, random forests, time series prediction, and clustering as well as a number of different deep learning networks such as convolutional neural networks, auto-encoder, or generative adversarial networks. The degree of detail and theory is such that all methods can be understood and critically discussed, and it is reinforced by extensive examples within materials science and engineering. The final part considers six complex applications and advanced topics of machine learning and data mining in materials science and engineering. A comprehensive appendix is included, summarizing the most important statistical and mathematical techniques.


Introduces machine learning/deep learning methods in detail based on examples and data from materials science;
Covers all theoretical foundations in an accessible manner, tailored to materials scientists and engineers;
Maximizes intuitive understanding with materials science and physics examples, coding exercises, and online material.
mehr
Verfügbare Formate
BuchGebunden
EUR96,29
E-BookPDF1 - PDF WatermarkE-Book
EUR96,29

Produkt

KlappentextThis text covers all of the artificial intelligence, deep learning, and data science topics relevant to materials science and engineering, accompanied by numerous examples and applications. The book begins with a concise introduction to statistics and probabilities, explaining important concepts and definitions such as probability functions and distributions, sampling and data preparation, Bayes' theorem, and statistical significance testing in the context of materials science. As such it is a useful introduction for both undergraduate and graduate students as well as a refresher for research scientists and practicing engineers. The second part is a detailed description of (statistical) machine learning and deep learning. It considers a range of supervised and unsupervised methods including multi-output regression, random forests, time series prediction, and clustering as well as a number of different deep learning networks such as convolutional neural networks, auto-encoder, or generative adversarial networks. The degree of detail and theory is such that all methods can be understood and critically discussed, and it is reinforced by extensive examples within materials science and engineering. The final part considers six complex applications and advanced topics of machine learning and data mining in materials science and engineering. A comprehensive appendix is included, summarizing the most important statistical and mathematical techniques.


Introduces machine learning/deep learning methods in detail based on examples and data from materials science;
Covers all theoretical foundations in an accessible manner, tailored to materials scientists and engineers;
Maximizes intuitive understanding with materials science and physics examples, coding exercises, and online material.
Details
Weitere ISBN/GTIN9783031465659
ProduktartE-Book
EinbandartE-Book
FormatPDF
Format Hinweis1 - PDF Watermark
FormatE107
Erscheinungsjahr2024
Erscheinungsdatum08.05.2024
Auflage2024
SpracheEnglisch
Dateigrösse39569 Kbytes
IllustrationenXXVI, 618 p. 200 illus. in color.
Artikel-Nr.14809607
Rubriken
Genre9200

Inhalt/Kritik

Inhaltsverzeichnis
Introduction to Data, Data Mining, and Prediction in Materials Science.- A Primer on Probabilities, Distributions and Statistics.- Introduction to Statistical Machine Learning.- Artificial Neural Networks and Deep Learning.- Advanced Topics of Machine Learning and Data Mining in Materials Science and Engineering.- Conclusion Outlook.- Appendix.mehr

Autor

Prof. Dr. Stefan Sandfeld is Director of the Institute for Advanced Simulation: Materials Data Science and Informatics (IAS-9) Forschungszentrum Juelich, Germany; and Professor/Chair of Materials Data Science and Materials Informatics, RWTH Aachen University.¿










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