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Mechanistic data science for STEM ed...

Liu, W. K.

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Mechanistic data science for STEM education and applications

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Mechanistic data science for STEM education and applications/ by Wing Kam Liu, Zhengtao Gan, Mark Fleming.
作者:	Liu, W. K.
其他作者:	Gan, Zhengtao.
出版者:	Cham :Springer International Publishing : : 2021.,
面頁冊數:	xv, 276 p. :ill., digital ;24 cm.
內容註:	1-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.
Contained By:	Springer Nature eBook
標題:	Mathematics. -
電子資源:	https://doi.org/10.1007/978-3-030-87832-0
ISBN:	9783030878320

Mechanistic data science for STEM education and applications
Liu, W. K.

Mechanistic data science for STEM education and applications[electronic resource] /by Wing Kam Liu, Zhengtao Gan, Mark Fleming. - Cham :Springer International Publishing :2021. - xv, 276 p. :ill., digital ;24 cm.

1-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.

This 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.

ISBN: 9783030878320

Standard No.: 10.1007/978-3-030-87832-0doiSubjects--Topical Terms:

515831
Mathematics.

LC Class. No.: QA39.3 / .L58 2021

Dewey Class. No.: 510

Mechanistic data science for STEM education and applications
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505 0 $a 1-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.
520 $a This 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.
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