Machine Learning for Cyber-Physical Systems Selected papers from the International Conference ML4CPS 2023

• Main Authors: Niggemann, Oliver, Beyerer, Jürgen, Krantz, Maria, Kühnert, Christian
• Format: eBook
• Language: English
• Published: Cham Springer Nature 2024; Springer
• Edition: 1
• Series: Technologien für die intelligente Automation
• Subjects: Applied mathematics; Artificial intelligence; Automatic validation; Computer hardware; Computer science; Computing and Information Technology; Cyber-physical systems; Cybernetics and systems theory; Electronics and communications engineering; Electronics engineering; Information theory; Machine learning; Mathematical modelling; Mathematics; Mathematics and Science; Network architecture; Neural networks; Reference, Information and Interdisciplinary subjects; Research and information: general; Technology, Engineering, Agriculture, Industrial processes
• ISBN: 9783031470615; 3031470613; 9783031470622; 3031470621
• DOI: 10.1007/978-3-031-47062-2

This open access proceedings presents new approaches to Machine Learning for Cyber-Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS – Machine Learning for Cyber-Physical Systems, which was held in Hamburg (Germany), March 29th to 31st, 2023. Cyber-physical systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments. This is an open access book.