AI-Based Surrogate Models of Digital Twins for Industrial Processes

Digital twins represent virtual replicas of physical systems, integrating real-time data and advanced analytics to monitor, simulate, and optimize industrial processes. This research delves into the application of AI-based surrogate models to improve the efficiency and accuracy of digital twins for industrial processes. The study employs machine learning techniques to develop computationally efficient models that maintain high accuracy. The integration of advanced sampling techniques and challenges related to data quality and interpretability are highlighted, proposing solutions to improve model robustness and reliability.