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Topic 

What is the role of AI/ML in Digital Twin Engineering?

Abstract

Digital Twins (DT) and Artificial Intelligence (AI)/Machine Learning (ML) have emerged over the last decade as key technologies at the core of the digital transformation to enhance decision-making and improve/optimize different aspects of systems and processes. On one side, DT provides a virtual representation of a physical object, system, or process that mirrors its real-world counterpart, referred to as the Actual Twin (AT). This representation is created and kept in synchrony with the AT using data collected from various sources, such as sensors, IoT devices, APIs, and historical records. The purpose of a DT is to provide services, like real-time monitoring, analysis, and simulation, using different types of specialized models. On the other hand, AI/ML empower organizations to leverage vast amounts of data to support better decision-making, enhanced efficiency, and drive innovation. These techniques can be used to analyze large datasets, both structured and unstructured, to discover patterns, trends, and insights that humans may overlook. Additionally, AI/ML capabilities enable real-time data processing, delivering immediate insights that support timely decision-making and operational adjustments.

However, while AI and ML are key technologies that can create advanced capabilities and serve as significant differentiators for products or businesses, they do not inherently solve problems on their own. They require high-quality data and effective mechanisms to ensure that AI/ML components meaningfully enhance specific aspects of a system or process. Essentially, AI/ML can be viewed as advanced technologies that contribute to developing core system components, which must be integrated into an overarching system that provides the necessary quality data and mechanisms for system and process improvements.

This guest lecture will focus on the role of AI and ML in the engineering of DTs, as well as the key challenges and opportunities associated with their application in this context. I will specifically explore how AI and ML can be utilized "in the DT" to deliver essential advanced services and "for the engineering of the DT" to automate specific engineering tasks. Additionally, I will provide an overview of various industrial DT research projects where we are leveraging the power of AI and ML.

About the speaker

The research of Professor Francis Bordeleau focuses on systematically improving industrial DevOps processes and developing a Digital Twin Engineering framework that leverages DevOps and AI/ML techniques. This framework aims to support the engineering and evolution of digital twins across various application domains. With over 30 years of experience in research, consulting, and collaboration with companies worldwide, he has expertise in software engineering, software process improvement, and Model-Based Engineering (MBE). Prior to his role at ETS, Francis served as the Product Manager of Software Development at Ericsson from 2013 to 2017, founded and led Zeligsoft from 2003 to 2013, and was an assistant professor at Carleton University from 1997 to 2006. Francis has been part of the organizing and program committee of many international conferences and workshops.

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Your host

Loek Cleophas, assistant Professor in Engineering of Software Intensive Systems of the department of M&CS, will host Professor Franics Bordeleau of Software Engineering, Kaloom-TELUS 脡TS Industrial Research Chair in DevOps.

Registration is required but free of charge.