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020 _a9783031465659
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024 7 _a10.1007/978-3-031-46565-9
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072 7 _aTEC021000
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100 1 _aSandfeld, Stefan.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_976501
245 1 0 _aMaterials Data Science
_h[electronic resource] :
_bIntroduction to Data Mining, Machine Learning, and Data-Driven Predictions for Materials Science and Engineering /
_cby Stefan Sandfeld.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2024.
300 _aXXVI, 618 p. 200 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aThe Materials Research Society Series,
_x2730-7379
505 0 _aIntroduction 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.
520 _aThis 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. .
650 0 _aMaterials
_xAnalysis.
650 0 _aData mining.
_96941
650 0 _aArtificial intelligence.
_958441
650 0 _aData structures (Computer science).
_966708
650 0 _aInformation theory.
_957783
650 1 4 _aMaterials Characterization Technique.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aArtificial Intelligence.
_958441
650 2 4 _aData Structures and Information Theory.
_966709
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031465642
776 0 8 _iPrinted edition:
_z9783031465666
776 0 8 _iPrinted edition:
_z9783031465673
830 0 _aThe Materials Research Society Series,
_x2730-7379
_976502
856 4 0 _uhttps://doi.org/10.1007/978-3-031-46565-9
912 _aZDB-2-CMS
912 _aZDB-2-SXC
999 _c59704
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