Year: 2024
Type: conference-paper
Abstract: 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.
Authors: Emmanuel Lwele, Alex Shenfield
Institution: Sheffield Hallam University
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4405756231