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Path Catalog
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This learning path equips senior and graduate students with the knowledge and skills to model complex systems using system dynamics. It covers core concepts such as feedback loops, stocks and flows, and simulation with industry-standard tools like Vensim and Stella. The path emphasizes systems thinking and practical model building, preparing learners to apply system dynamics to real-world problems.
This graduate-level learning path equips learners with advanced optimization techniques for systems engineering. It covers convex optimization foundations, heuristic methods, and robust optimization, emphasizing practical application to complex systems problems. Learners will progress from mathematical prerequisites through core theories to advanced applications and research frontiers.
This graduate-level learning path provides a systematic exploration of complex systems, focusing on complexity metrics, emergence, self-organization, and network theory. It builds from foundational systems theory and dynamical systems to advanced topics and applications in systems engineering, with an emphasis on understanding and managing emergent behavior.
This graduate-level learning path provides a rigorous grounding in the theoretical foundations of systems engineering, emphasizing general systems theory, cybernetics, and control theory. It systematically develops systems thinking, modeling, and analysis skills necessary for advanced study and research in systems engineering.
This advanced learning path equips senior and graduate students with the knowledge and skills to apply systems engineering principles to enterprise-level systems. It covers systems thinking, the systems engineering lifecycle, enterprise architecture frameworks, and the alignment of business strategy with technical implementation, culminating in managing enterprise transformation.
A graduate-level learning path applying systems engineering principles to defense and aerospace contexts. It covers foundational systems engineering, lifecycle and management, specialized defense processes, security, and acquisition, culminating in a capstone application.
This path equips senior systems engineering students with advanced quantitative methods for decision-making under uncertainty. It covers Bayesian probability and updating, real options analysis for flexible investments, and robust decision-making approaches including info-gap and scenario planning. The path emphasizes practical application through decision trees, Monte Carlo simulation, and sensitivity analysis, culminating in an integrated capstone project.
This learning path equips senior engineering students with the knowledge and skills to apply Model-Based Systems Engineering (MBSE) using the Systems Modeling Language (SysML). Starting from systems thinking fundamentals, it progresses through SysML diagram types, modeling methodology, and practical application with industry tools like MagicDraw and Rhapsody.
A comprehensive learning path for senior university students to design advanced system architectures, covering foundational principles, modular design, service-oriented architecture, trade-off analysis, and practical application through case studies and projects.
This learning path introduces junior university students to the core principles and quantitative methods of systems reliability engineering. It covers basic probability and statistics foundations, reliability models for components and systems, failure analysis techniques, and the related concepts of maintainability and availability, culminating in a practical project that applies these concepts.