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  2. Matematik
  3. Sandsynlighedsregning og statistik

Mechanistic Data Science for STEM Education and Applications

Af: Zhengtao Gan, Wing Kam Liu, Mark Fleming Engelsk Paperback

Mechanistic Data Science for STEM Education and Applications

Af: Zhengtao Gan, Wing Kam Liu, Mark Fleming Engelsk Paperback
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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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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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Se mere i:
Produktdetaljer
Sprog: Engelsk
Sider: 276
ISBN-13: 9783030878344
Indbinding: Paperback
Udgave:
ISBN-10: 3030878341
Kategori: Sandsynlighedsregning og statistik
Udg. Dato: 23 dec 2022
Længde: 0mm
Bredde: 155mm
Højde: 235mm
Forlag: Springer Nature Switzerland AG
Oplagsdato: 23 dec 2022
Forfatter(e): Zhengtao Gan, Wing Kam Liu, Mark Fleming
Forfatter(e) Zhengtao Gan, Wing Kam Liu, Mark Fleming

Kategori Sandsynlighedsregning og statistik

ISBN-13 9783030878344

Sprog Engelsk

Indbinding Paperback

Sider 276

Udgave

Længde 0mm

Bredde 155mm

Højde 235mm

Udg. Dato 23 dec 2022

Oplagsdato 23 dec 2022

Forlag Springer Nature Switzerland AG

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