04411nam a22005775i 4500 978-3-031-46565-9 DE-He213 20251030112031.0 cr nn 008mamaa 240508s2024 sz | s |||| 0|eng d 9783031465659 978-3-031-46565-9 10.1007/978-3-031-46565-9 doi TA418.5-.84 TGMT bicssc TEC021000 bisacsh TGMT thema Sandfeld, Stefan. author. aut http://id.loc.gov/vocabulary/relators/aut 76501 Materials Data Science [electronic resource] : Introduction to Data Mining, Machine Learning, and Data-Driven Predictions for Materials Science and Engineering / by Stefan Sandfeld. 1st ed. 2024. Cham : Springer International Publishing : Imprint: Springer, 2024. XXVI, 618 p. 200 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda The Materials Research Society Series, 2730-7379 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. 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. . Materials Analysis. Data mining. 6941 Artificial intelligence. 58441 Data structures (Computer science). 66708 Information theory. 57783 Materials Characterization Technique. Data Mining and Knowledge Discovery. Artificial Intelligence. 58441 Data Structures and Information Theory. 66709 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031465642 Printed edition: 9783031465666 Printed edition: 9783031465673 The Materials Research Society Series, 2730-7379 76502 https://doi.org/10.1007/978-3-031-46565-9 ZDB-2-CMS ZDB-2-SXC 59704 59704