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010   $a3-030-87832-5
024 7 $a10.1007/978-3-030-87832-0
035   $a(MiAaPQ)EBC6839008
035   $a(Au-PeEL)EBL6839008
035   $a(CKB)20275219100041
035   $a(OCoLC)1292352872
035   $a(PPN)259390658
035   $a(DE-He213)978-3-030-87832-0
035   $a(EXLCZ)9920275219100041
100   $a20211221d2021      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aMechanistic Data Science for STEM Education and Applications /$fby Wing Kam Liu, Zhengtao Gan, Mark Fleming
205   $a1st ed. 2021.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021.
215   $a1 online resource (287 pages)
225 1 $aMathematics and Statistics Series
311 08$aPrint version: Liu, Wing Kam Mechanistic Data Science for STEM Education and Applications Cham : Springer International Publishing AG,c2021 9783030878313 
320   $aIncludes bibliographical references and index.
327   $a1-Introduction to Mechanistic Data Science -- 2-Multimodal Data Generation and Collection -- 3-Optimization and Regression -- 4-Extraction of Mechanistic Features -- 5-Knowledge-Driven Dimension Reduction and Reduced Order Surrogate Models -- 6-Deep Learning for Regression and Classification -- 7-System and Design.
330   $aThis book introduces Mechanistic Data Science (MDS) as a structured methodology for combining data science tools with mathematical scientific principles (i.e., ?mechanistic? principles) to solve intractable problems. Traditional data science methodologies require copious quantities of data to show a reliable pattern, but the amount of required data can be greatly reduced by considering the mathematical science principles. MDS is presented here in six easy-to-follow modules: 1) Multimodal data generation and collection, 2) extraction of mechanistic features, 3) knowledge-driven dimension reduction, 4) reduced order surrogate models, 5) deep learning for regression and classification, and 6) system and design. These data science and mechanistic analysis steps are presented in an intuitive manner that emphasizes practical concepts for solving engineering problems as well as real-life problems. This book is written in a spectral style and is ideal as an entry level textbook for engineering and data science undergraduate and graduate students, practicing scientists and engineers, as well as STEM (Science, Technology, Engineering, Mathematics) high school students and teachers.
410  0$aMathematics and Statistics Series
606   $aEngineering mathematics
606   $aQuantitative research
606   $aComputational intelligence
606   $aSampling (Statistics)
606   $aEngineering design
606   $aEngineering Mathematics
606   $aData Analysis and Big Data
606   $aComputational Intelligence
606   $aMethodology of Data Collection and Processing
606   $aEngineering Design
615  0$aEngineering mathematics.
615  0$aQuantitative research.
615  0$aComputational intelligence.
615  0$aSampling (Statistics)
615  0$aEngineering design.
615 14$aEngineering Mathematics.
615 24$aData Analysis and Big Data.
615 24$aComputational Intelligence.
615 24$aMethodology of Data Collection and Processing.
615 24$aEngineering Design.
676   $a510
700   $aLiu$b W. K$g(Wing Kam),$0297756
702   $aFleming$b Mark$f1969-
702   $aGan$b Zhengtao
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910520077303321
996   $aMechanistic Data Science for STEM Education and Applications$92584223
997   $aUNINA