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portada Machine Learning for Engineers: Introduction to Physics-Informed, Explainable Learning Methods for ai in Engineering Applications
Type
Physical Book
Publisher
Author
Language
English
Pages
241
Format
Paperback
ISBN13
9783662699942
Edition No.
2024th

Machine Learning for Engineers: Introduction to Physics-Informed, Explainable Learning Methods for ai in Engineering Applications

Marcus J. Neuer (Author) · Springer · Paperback

Machine Learning for Engineers: Introduction to Physics-Informed, Explainable Learning Methods for ai in Engineering Applications - Marcus J. Neuer

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Synopsis "Machine Learning for Engineers: Introduction to Physics-Informed, Explainable Learning Methods for ai in Engineering Applications"

Machine learning and artificial intelligence are ubiquitous terms for improving technical processes. However, practical implementation in real-world problems is often difficult and complex. This textbook explains learning methods based on analytical concepts in conjunction with complete programming examples in Python, always referring to real technical application scenarios. It demonstrates the use of physics-informed learning strategies, the incorporation of uncertainty into modeling, and the development of explainable, trustworthy artificial intelligence with the help of specialized databases. Therefore, this textbook is aimed at students of engineering, natural science, medicine, and business administration as well as practitioners from industry (especially data scientists), developers of expert databases, and software developers.

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