Tutorials

Room: Acadia A
8:00 AM - 10:00 AM

*This tutorial will be presented remotely.

A story doesn't just tell words; it connects them and conveys the meaning to the read/listening audience. Technology and people's interaction with it depend on an understanding of how technical designs fit together harmoniously, rather than just integrating people into new tech. Research and innovations may risk effective communication to their audience. This tutorial will demonstrate how research studies can be expressed as data stories, not just technical artefacts, but as human-centered communication systems embedded in social contexts.

Room: Acadia B
8:00 AM - 12:00 PM

System Architecture Optimization (SAO) applies numerical optimization algorithms to the generation and evaluation of system architectures. As systems such as aircraft and land vehicles grow in complexity and adopt novel technologies, it becomes essential to systematically explore architectural design spaces without relying on domain-specific heuristics that may introduce bias and limit innovation. SAO moves optimization methods—traditionally applied downstream after a small number of architectures are selected—into the upstream architecture decision-making process.

By leveraging system architecting activities such as functional decomposition and function-to-component allocation, SAO enables integration between Model-Based Systems Engineering (MBSE) descriptive models and quantitative, simulation-based evaluation and optimization. As a result, SAO bridges conceptual design and architecting with downstream sizing and optimization while enriching MBSE models with data from disciplinary analyses.

This tutorial introduces the theoretical foundations of SAO and provides hands-on experience in modeling architecture design spaces, coupling architecture generation with evaluation code, selecting appropriate optimization algorithms, and integrating SAO with Capella, an open-source MBSE platform.

Room: Acadia C
8:00 AM - 12:00 PM

The tutorial will address assurance and control as the means to achieve secure system function over tactical threat response. As systems become more complex, the strategic approach re: assurance is needed to gain advantage, an approach only the systems engineer or those practicing systems engineer may hope to achieve.

Room: Acadia A
1:00 PM - 5:00 PM

*This tutorial will be a pre-recorded video provided by the speaker.

As today’s systems become increasingly complex and interconnected, traditional, reactive security approaches are no longer sufficient. Security must be engineered into systems from the beginning - not added after deployment - to address sophisticated adversarial threats and meet certification, accreditation, and trust requirements.

This tutorial introduces Systems Security Engineering (SSE), as defined in DoDI 5200.44 and NIST SP 800-160. SSE integrates security directly into the systems engineering life cycle, applying rigorous engineering principles to identify vulnerabilities, reduce risk, and build resilient, trustworthy systems.

Participants will gain a system-level and management-level understanding of information security, including how security requirements are derived, how they are integrated into system design and development, and how security is managed across the system life cycle. The session balances technical foundations with organizational and compliance considerations, equipping attendees to align security with mission objectives.

By the end of the tutorial, participants will be better prepared to contribute to secure system development and to advocate for security as a core engineering discipline in the face of evolving threats.

Room: Acadia B
1:00 PM - 5:00 PM

3D perception, the ability to perceive depth and spatial relationships in the world, is fundamental to human cognition and holds immense potential across various sensing domains, including robotics and intelligent vehicles. The emergence of deep learning–based techniques offers a compelling alternative, potentially enabling 3D vision from monocular camera inputs without additional hardware modifications. This tutorial will delve into the principles and applications of traditional 3D sensing and computer vision methods for intelligent vehicles. It will then introduce predictive 3D sensing based on deep learning, covering fundamental concepts, common architectures, and training data requirements. Simultaneous localization and mapping (SLAM) and autonomous driving will be used as illustrative examples. Issues such as compatibility with monocular cameras and seamless integration into existing sensor systems without requiring additional hardware modifications will also be discussed.

Room: Acadia C
1:00 PM - 5:00 PM

*The presenter will be remote, but there will be an in-person facilitator.

Complex systems must often evolve over time to address new requirements, technologies, and operating conditions. Designing for adaptability requires balancing current system needs with potential future changes. This tutorial introduces key concepts and theory behind system adaptability and reviews current work in the field.

Building on the introductory tutorial presented in 2025, this session emphasizes hands-on learning. Participants will actively apply adaptive system design methods using a starting model based on the INCOSE CubeSat Reference Model (CSRM). Through guided exercises, attendees will explore the three fundamental factors of adaptive system design: Mission and Requirements Space (MRES), Design Space, and Switching Cost. Using these concepts, participants will evaluate alternatives and develop a recommended design path.

The tutorial is intended for participants of all experience levels. Prior knowledge of Model-Based Systems Engineering (MBSE) may be helpful but is not required, as exercises will be completed individually or in small groups.