- Type Validation
- Level Advanced
- Time Weeks
- Cost Paid
Cyber-Physical Systems Modeling
Issued by
University of Connecticut
Earners of the Cyber-Physical Systems Modeling Badge have developed skills in defining fundamental physical and mathematical representations of heat transfer, fluid transport, separations in large-scale systems. They exhibit proficiency in simulating systems at different levels of complexity and are comfortable with concepts of acausal, equation-oriented modeling and have become knowledgeable of the role of modeling abstraction, reduction, and meta-modeling for complex systems.
- Type Validation
- Level Advanced
- Time Weeks
- Cost Paid
Skills
- Computational Engineering
- Control Systems Architecting
- Cyber-Physical Systems Engineering
- Hybrid Systems Modeling
- Mathematical Approximation
- Mathematical Modeling
- Meta System Modeling
- Model-Based Design
- Modeling Abstractions
- Multi-Phase Modeling
- Multi-Scale Modeling
- Nonlinear Systems Modeling
- Systems Engineering
- System Simulation
- Systems Modeling
- Uncertainty Quantification
- Validation
- Verification
Earning Criteria
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Badge earners complete SE-5101 Acausal Physical Systems Modeling course at the University of Connecticut, which is a hybrid-online graduate course that can be taken from anywhere in the world. Earners can take this graduate course as a matriculated UConn graduate student or as a non-degree graduate student, which does not require admission to the UConn graduate school. Badge holders complete a course-long project and must earn a B- or better on this project to earn the badge.
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Badge earners can design techniques for stability, safety, liveness, and other specifications for a cyber-physical system through control design, can conduct modeling, verification, and control of systems containing discrete and continuous components of hybrid systems, and can design and operate networks of sensors, actuators, and distributed computation. See Standard [1] NAE-CPS below.
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Badge earners can develop and use systematic mathematical approaches for constructing nonlinear empirical models informed by physics principles, possibly including physically imposed constraints and can develop and update verification and validation methods for cyber-physical designs, systems, and models. See Standard [1] NAE-CPS below.
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Badge earners can develop analytical and computational approaches needed to understand and model the behavior of complex multi-physics, and multi-scale phenomena and can develop and use the corresponding analytical tools and computational approaches needed to quantify the impact of the fidelity of finer-scale models on large-scale dynamics. See Standard [2] DOE-SIAM below.
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Badge earners can perform modeling and analysis to design and predict operating characteristics for a complex system and predict when changing meta system conditions cause system failures, can define and quantify uncertainty in systems flows and processes for a systems model based upon reasoning under uncertainty, and can conduct sensitivity analysis for a complex system using a model during early phase design. See Standard [2] DOE-SIAM below.
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Badge earners can apply computational engineering processes to a real-world problem using a modeling approach, can apply computational engineering methods and principles to the design and operation of a cyber-physical systems, and can contribute to the model development and interpretation activities. See Standard [3] INCOSE ISECF below.
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Badge earners complete a course-long project consisting of a proposal, midterm report, final report, and systems model artifact. The project refers mainly to identification, challenge quantification, significance, and relevance to the model-based design (MBD) philosophy, plan of attack, system modeling, system optimization using cross-platform programming. The final presentation should identify all the aforementioned elements in a quantifiable manner and suggest a strategy for solution.
Standards
A 21st Century Cyber-Physical Systems Education. Committee on 21st Century Cyber-Physical Systems Education; Computer Science and Telecommunications Board; Division on Engineering and Physical Sciences; National Academies of Sciences, Engineering, and Medicine. ISBN 978-0-309-45163-5 | DOI: 10.17226/23686.
SIAM APPLIED MATHEMATICS AT THE U.S. DEPARTMENT OF ENERGY: Past, Present and a View to the Future. A Report by an Independent Panel from the Applied Mathematics Research Community May 2008.
INCOSE Model-Based Enterprise Capabilities Matrix 2.0b Draft June 2019 r4. Joe Hale, NASA