Offered: Fall 2025 (current)
Introduction to System Modeling, Building useful models, Steps of Modeling. Discrete event simulation. Petri Net and Modeling of systems. Properties of Petri Net and Analysis Techniques. Petri Net Extensions, Timed Petri Net. Homogeneous Markov SPN. Machine Learning in System Modeling, Quantum Machine Learning and Modeling, Computation of Measures of Reliability and Performance. Performance/Reliability Modelling through SPN. Validation and Verification of Models.
The core objectives of this course are to:
To model, analyze, and optimize discrete event systems and other stochastic systems using Petri Nets
To understand workflow automation, communication networks, and reliability modelling using basic and extended Petri Net models
To integrate modern computational tools (Python, SimPy, NetworkX) and apply machine learning (ML) and quantum computing (QML) to enhance traditional system modeling techniques to solve real-world problems
To build system models, validate performance, and explore real-world applications in various fields through a hands-on, project-based learning approach
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