AI- and ML-driven intelligent design of digital twins
subject: Artificial intelligence; Data integration; Digital twins,; Machine learning,; Predictive maintenance
description: Digital twins (DTs), or virtual copies of real-world systems, have changed and improved many industries in terms of monitoring, analysis, and optimization in real time. Artificial intelligence (AI) and machine learning (ML) together have significantly enhanced the functionalities of DTs so that they become more efficient and versatile decision-making and process improvement tools. The production and application of DTs most importantly rely on AI and ML. Such technologies allow integration and analysis of very large amounts of data from various sources and provide an overview of the physical system. The personnel involved in the company may gain deeper insights into overall business processes and identify changes that would remain unknown when applying the traditional methods, based on the employment of the capabilities of AI-based integration and data analysis. An essential example of ML use cases in the framework of DTs is predictive maintenance. Any ML algorithm can resort to historical data and immediate sensor data to predict potential failures of application equipment and propose a repair schedule, significantly reducing operational downtime and refining the distribution of resources. The AI-powered optimization and simulation methods can give organizations the possibility to consider numerous scenarios and identify the most effective ways to resolve complex issues. The DTs are AI-enabled and can detect and decide on the fly, which allows them to react to changing conditions instantly and prevent some of the issues before they happen. In addition, AI-powered predictive analysis and risk management allow the firms to go a step ahead and address the potential problems in advance by developing effective risk reduction strategies. DTs are mainly constructed with AI and ML in various industries. In the context of manufacturing and Industry 4.0, DTs play an important role in optimizing production and increasing the quality control standards. Urban planners use the DT…
creator: Manasa, R.; Navya, K.; Keshavamurthy, R.; Madhura, R.; Mahesh Kumar, N.
source: Digital Twins for Sustainable Development;pp.1-33
publisher: Elsevier
date: 01-01-2026
rights: Restricted Access; Hardcopy may be available in the library
relation: ISBN: 978-044327388-9; 978-044327389-6;
language: English
type: Book chapter
coverage: Manasa R., Department of Electronics and Communication Engineering, Dayananda Sagar College of Engineering, Bengaluru, India; Navya K., Department of Electronics and Communication Engineering, Dayananda Sagar College of Engineering, Bengaluru, India; Keshavamurthy R., Department of Mechanical and Automobile Engineering, CHRIST (Deemed to be University), Bengaluru, India; Madhura R., Department of Electronics and Communication Engineering, Dayananda Sagar College of Engineering, Bengaluru, India; Mahesh Kumar N., Department of Information Science and Engineering, Global Academy of Technology, Bengaluru, India