Tutorials

A simulation can be a complex product in its own right, with its own architecture of models, tools, and hardware. It has a distinct development cycle, from specification to validation, and involves its own critical decisions. However, we've all been there: simulation development can rely on informal practices. The good news:  applying systems engineering principles can of course help. In this tutorial, we'll explore how.

After a refresher on key systems engineering concepts (requirements quality, functional and non-functional requirements, Operational Design Domain, or MBSE languages and tools), we'll dive into defining simulation needs and writing simulation specifications. We’ll discuss the application of different good practices, such as defining the system to be represented or the simulation's purpose, while accounting for factors like the system's development phase and decision criticality. The tutorial will also address strategies to build credibility, with a focus on verification and validation. We'll cover relevant standards like NASA-STD-7009 and the Predictive Capability Maturity Model.

Throughout the tutorial, traceability will be emphasized as a cornerstone for quality management and automation. Real-world automotive use cases, contributed by leading companies, will be used as practical examples throughout. Attendees will be encouraged to share their own experiences, challenges, and best practices. The discussions we'll have during the tutorial could be compiled into a report to benefit the wider community. This tutorial builds on the success of sessions at IEEE RASSE 2021 and IEEE SysCon 2023.

After initial development, Complex Systems are updated and changed to address new requirements and conditions. Designing for Adaptability requires evaluating trade-offs between the current requirements and potential future requirements. This tutorial will provide training on basic concepts and theory of system adaptability, and its application in trade studies to minimize costs from later changes.

This tutorial will lead participants through the steps in applying for and executing an Academic Equivalency (AcEq) agreement between a university and the International Council on Systems Engineering (INCOSE). AcEq is a means for a university to have its course(s) recognized as equivalent to the INCOSE knowledge exam in qualifying students for INCOSE Certification. Universities who apply for AcEq must prove to INCOSE reviewers that their course assesses against the same topics as the knowledge exam. This tutorial will review the requirements and include tips from the AcEq creator and program manager on how to be most efficient in applying. The conversation will be best-suited for faculty who teach systems engineering, though there is no requirement that their course be in an SE department. Industry partners are also welcome to come to learn about AcEq in hopes to convince their local universities to apply.

With the growing adoption of electric transportation, it is becoming increasingly crucial to understand and address performance degradation to maintain reliability and efficiency. This tutorial focuses on the data-driven modeling of a wheel motor Electric Bus (E-Bus). A model-based data-driven method is explored to estimate the traction energy and reliability performance of a battery-powered E-Bus. The modeling process takes into account various factors such as loading operations, and driving behavior, which are critical for accurate energy and reliability predictions. This comprehensive model aims to predict the energy and reliability and improve operational efficiency by leveraging onboard sensor data. The tutorial demonstrates how data-driven modeling can be effectively used to enhance the performance and efficiency of electric transportation systems. This approach not only contributes to the sustainability of transient operations but also sets a precedent for the broader application of data-driven methods in the electric transportation industry. Furthermore, the tutorial highlights the importance of continuous monitoring and adaptive control strategies. This proactive approach ensures that the E-bus can operate at peak efficiency, reducing downtime and maintenance costs.

The field of Stochastic Systems, Control, and Games is inherently interdisciplinary and multidisciplinary, playing a critical role in numerous applications. These include brain disorders and broader biomedical fields, financial markets and actuarial sciences, networks and autonomous vehicles, climate change, and sustainable co-development in small villages. Understanding randomness in systems, whether in the human brain or stock markets, is a research area that demands advanced knowledge of probability and stochastic process theory. Core disciplines such as probability, stochastic processes, stochastic modeling, and mathematical statistics form the foundation for these applications. This minitutorial provides a concise yet thorough introduction to advanced stochastic optimal control and mean-field-type game theory, focusing on systems driven by the Rosenblatt process - a type of noise that is non-Gaussian, non-Poissonian, and non-Markovian. Applications discussed include smart energy systems and blockchain token economics. The speakers are experienced in preparing students and postdocs at all levels for successful professional careers in these fields.