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The role of the tutorials is to provide a platform for a more intensive scientific exchange amongst researchers interested in a particular topic and as a meeting point for the community. Tutorials complement the depth-oriented technical sessions by providing participants with broad overviews of emerging fields. A tutorial can be scheduled for 1.5 or 3 hours.

Tutorial on
Introduction to Model-Based Systems Engineering (MBSE) for Digital Twins of Cyber-Physical Systems


Shaukat Ali
Simula Research Laboratory
Brief Bio
Shaukat Ali is a Chief Research Scientist/Research Professor at Simula Research Laboratory, Norway. His expertise includes devising novel methods for Verification and Validation of Cyber-Physical Systems. He has been involved in several basic research, research-based innovation, and innovation projects in the capacity of PI/Co-PI related to Model-based Testing, Search-Based Software Engineering, Model-Based System Engineering, and Uncertainty Modeling & Testing. He has rich experience of working in several countries: the UK, Canada, Norway, Japan, and Pakistan. He is also actively participating in defining international standards on software modeling in Object Management Group (OMG), notably a new standard on Precise Semantics for Uncertainty Modeling.
Tao Yue
Simula Research Lab
Brief Bio
Tao Yue is a chief research scientist of Simula Research Laboratory, Oslo, Norway and she is also affiliated to University of Oslo. She has received the PhD degree in the Department of Systems and Computer Engineering at Carleton University, Ottawa, Canada in 2010. Before that, she was an aviation engineer and system engineer for seven years. She has nearly 20 years of experience of conducting industry-oriented research with a focus on Model-Based Engineering (MBE) in various application domains such as Avionics, Maritime and Energy, Communications, Automated Industry, and Healthcare in several countries including Canada, Norway, and China. Her present research area is software engineering, with specific interests in Requirements Engineering, MBE, Model-based Testing, Uncertainty-wise Testing, Uncertainty Modeling, Search-based Software Engineering, Empirical Software Engineering, and Product Line Engineering, with a particular focus on large-scale software systems such as Cyber-Physical Systems.

Gartner names digital twins as one of the top technologies of 2019 that has the potential to transform the current form of industries and the public/governmental sectors in the near future [1]. The potential benefits of digital twins include significant enhancement of systems' and processes' quality, preventing severe failures, and reducing downtime of systems in many domains such as manufacturing, oil & gas, and maritime. The realization of digital twins relies on a multitude of approaches, tools, and technologies such as model-based systems engineering, artificial intelligence/machine learning, and simulation/co-simulation. This tutorial will focus on one of such approaches to develop, operate, and maintain digital twins with the model-based systems engineering approach, together with its underlying physical system. The focus will be on the presentation of relevant modeling notations, methods, and tools that are relevant for developing digital twins for Cyber-Physical Systems such as SysML, Modelica, and MARTE.  The relevant examples will also be used to explain the concepts.
 1.https://www.gartner.com/smarterwithgartner/gartner-top-10-strategic-technology-trends-for-2019/, Accessed January 3, 2020.


Digital Twins, Model-based Systems Engineering (MBSE), SysML, and UML

Aims and Learning Objectives

We aim to introduce the audience to MBSE-based development of digital twins. Since it is an introductory tutorial on digital twins, we will also focus on basic definitions of digital twins, various views on digital twins, and maturity levels of digital twins. The audience will also learn about key modeling notations and tools that are relevant to the development of digital twins with MBSE in addition to relevant algorithms such as artificial intelligence and machine learning algorithms that play a key role in the implementation, deployment, and operation of digital twins.

Target Audience

Both industrial and academic participants with limited or no knowledge about digital twins and cyber-physical systems.

Prerequisite Knowledge of Audience

Basic knowledge about modeling languages such as UML, SysML, etc.

Detailed Outline

- Part 1: Introduction to digital twins
1.1: Definitions
1.2: Functionalities of digital twins
1.3: Management of systems with digital twins

- Part 2: Introduction to model-based systems engineering (MBSE)
2.1: Definitions
2.2: Common modeling notations, methodologies, and tools

- Part 3: MBSE for Digital Twins for Cyber-Physical Systems
3.1: Cyber-Physical Systems and their Digital Twins
3.2: MBSE for Digital Twins
3.3: Simulation/Co-Simulations
3.4: Functionalities of digital twins for CPS

- Part 4: Conclusion and Future

Secretariat Contacts
e-mail: modelsward.secretariat@insticc.org