Maschinelles Lernen Für Die Ingenieurwissenschaften: Introduction to Physics-informed, Explainable Learning Methods for Ai in Engineering Applications - Brossura

Neuer, Marcus

Brossura

ISBN 10: 366269994X ISBN 13: 9783662699942

Casa editrice: Springer-Nature New York Inc, 2024

Riassunto

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.

Su questo libro

Informazioni sull?autore

Dr. Marcus J. Neuer has developed machine learning and explainable artificial intelligence for usable, profitable applications in various research and industry projects. He leads the research and development department at innoRIID GmbH and teaches at RWTH Aachen as well as the University of Applied Sciences for Business, FHDW. His algorithms are successfully used today in various products, including in the fields of nuclear safety and the process industry.

 

 

 

Dalla quarta di copertina

Machine learning and artificial intelligence are ubiquitous technologies 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 sciences, medicine, and business administration as well as practitioners from industry (especially data scientists), developers of expert databases, and software developers.

Excerpts from the Content

• Introduction to Working with Data
• Mathematical Fundamentals and Data Preprocessing
• Supervised and Unsupervised Learning Methods
• Physics-Informed and Stochastic Learning Methods
• Semantic Technologies and Explainable, Trustworthy Artificial Intelligence

The Author

Dr. Marcus J. Neuer has developed machine learning and explainable artificial intelligence for usable, profitable applications in various research and industry projects. He leads the research and development department at innoRIID GmbH and teaches at RWTH Aachen as well as the University of Applied Sciences for Business, FHDW. His algorithms are successfully used today in various products, including in the fields of nuclear safety and the process industry.

The translation was done with the help of artificial intelligence. A subsequent human revision was done primarily in terms of content.

Dettagli dell'edizione

• Editore: Springer-Nature New York Inc
• Data di pubblicazione: 2024
• Lingua: Inglese
• ISBN 10: 366269994X
• ISBN 13: 9783662699942
• Rilegatura: Copertina flessibile
• Numero di pagine: 277
• Contatto del produttore: ProductSafety@springernature.com; ProductSafety@springernature.com; ; Poststr. 9; Darmstadt; 64293; Germania