International Conference on Digital Twins and Machine Learning (ICDTML - 27)

This track focuses on the latest developments in digital twin technologies and their applications in various engineering fields. Participants will explore innovative approaches to creating and managing virtual replicas of physical systems.

This session will delve into advanced machine learning techniques that enhance predictive modeling capabilities. Researchers will present methodologies that improve accuracy and efficiency in forecasting outcomes in engineering applications.

This track emphasizes the integration of simulation and analytics in engineering processes. Attendees will discuss how these tools can optimize design and operational efficiency through data-driven insights.

This session will explore the use of supervised and unsupervised learning techniques in industrial contexts. Papers will highlight case studies and methodologies that demonstrate the effectiveness of these approaches in real-world scenarios.

This track investigates the application of deep learning algorithms for detecting anomalies in complex systems. Participants will share findings on how these techniques can enhance system reliability and safety.

This session focuses on the critical role of feature extraction and data preprocessing in machine learning workflows. Researchers will present innovative strategies for improving data quality and model performance.