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Computational Sciences and Artificial Intelligence in Industry: New Digital Technologies for Solving Future Societal and Economical Challenges 2022 ed. [Hardback]

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  • Formāts: Hardback, 275 pages, height x width: 235x155 mm, weight: 606 g, 71 Illustrations, color; 23 Illustrations, black and white; XV, 275 p. 94 illus., 71 illus. in color., 1 Hardback
  • Sērija : Intelligent Systems, Control and Automation: Science and Engineering 76
  • Izdošanas datums: 20-Aug-2021
  • Izdevniecība: Springer Nature Switzerland AG
  • ISBN-10: 3030707865
  • ISBN-13: 9783030707866
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  • Hardback
  • Cena: 180,78 €*
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Computational Sciences and Artificial Intelligence in Industry: New Digital Technologies for Solving Future Societal and Economical Challenges 2022 ed.Palielināt
  • Formāts: Hardback, 275 pages, height x width: 235x155 mm, weight: 606 g, 71 Illustrations, color; 23 Illustrations, black and white; XV, 275 p. 94 illus., 71 illus. in color., 1 Hardback
  • Sērija : Intelligent Systems, Control and Automation: Science and Engineering 76
  • Izdošanas datums: 20-Aug-2021
  • Izdevniecība: Springer Nature Switzerland AG
  • ISBN-10: 3030707865
  • ISBN-13: 9783030707866
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9783030707866.html
Keywords:

This book is addressed to young researchers and engineers in the fields of Computational Science and Artificial Intelligence, ranging from innovative computational methods to digital machine learning tools and their coupling used for solving challenging industrial and societal problems.This book provides the latest knowledge from jointly academic and industries experts in Computational Science and Artificial Intelligence fields for exploring possibilities and identifying challenges of applying Computational Sciences and AI methods and tools in industrial and societal sectors.

Chapter
1. Co-development of Methodology, Applications,and Hardware in Computational Science and Arti?cial Intelligence?.
Chapter
2. Novel Strategies for Data-driven Evolutionary Optimization.
Chapter
3. Arti?cial Intelligence and Computational Science.
Chapter
4. Supervised Learning and Applied Mathematics.
Chapter
5. Application of the Topological Gradient to Parsimonious Neural Networks.
Chapter
6. Generation of Error Indicators for Partial Di?erential Equations by Machine Learning Methods.
Chapter
7. Newton Method for Minimal Learning Machine.