Physically oriented modeling of heterogeneous systems
Mathematics and Computers in Simulation - Special issue on 3rd IMACS Symposium on Mathematical Modelling — 3rd MATHMOD Vienna
Continuous System Modeling
Executable UML: A Foundation for Model-Driven Architectures
Executable UML: A Foundation for Model-Driven Architectures
Hardware synthesis from guarded atomic actions with performance specifications
ICCAD '05 Proceedings of the 2005 IEEE/ACM International conference on Computer-aided design
A review of function modeling: Approaches and applications
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Cyber Physical Systems: Design Challenges
ISORC '08 Proceedings of the 2008 11th IEEE Symposium on Object Oriented Real-Time Distributed Computing
Cyber-physical systems: the next computing revolution
Proceedings of the 47th Design Automation Conference
Co-design of cyber-physical systems via controllers with flexible delay constraints
Proceedings of the 16th Asia and South Pacific Design Automation Conference
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The high complexity of cross-domain engineering in combination with the pressure for product innovation, higher quality, time-to-market, and budget constraints make it imperative for companies to use integrated engineering methods and tools. Computer engineering tools are mainly focused on a particular domain and therefore it is difficult to combine different tools for systemlevel analysis. This paper presents a novel approach and tool for integrated cyber-physical systems (CPS) design based on the FBS (Function-Behavior-State) methodology where multi-domain simulation models capturing both the behavioral-structural aspects of a system are automatically generated from its functional description. Our approach focuses on simulation-enabled FBS models using automatic and context-sensitive mappings of standard Functional Basis elementary functions to simulation components described in physical modeling languages (i.e. Modelica). Using a real electro-mechanical CPS application we demonstrate how our context-sensitive synthesis approach generates industry-quality executable functional models of higher quality than state-of-the-art approaches using manual mapping.