Modelling and Simulation (CSEG8003) — Lecture Notes

Course Objectives

Introduce computer simulation technologies and techniques, provide the foundations to understand simulation needs, and implement and test simulation and data-analysis libraries and programs. The emphasis is on building simulation software environments, not merely operating existing packages, and on modelling the layers of society's critical infrastructure networks together with the tools that view and control such simulations.

Course Outcomes

CO1: Discuss computer simulation needs, and implement and test simulation and data-analysis libraries and programs.
CO2: Design simulation software environments.
CO3: Recognise modelling layers of society's critical infrastructure networks.
CO4: Design tools to view and control simulations and their results.

How to Use These Notes

Each unit is a single reading document — not a slide deck.
Every topic is written in three layers: definition box, plain-language explanation, then numbered points, tables and examples.
Green Exam tip boxes say what is usually asked; amber Common mistake boxes say what loses marks.
Each unit closes with a summary, key terms and practice questions grouped by mark weight.
Use the sidebar to navigate, the Dark / Light button for night reading, and Print / PDF for a clean printable copy.

Unit-wise Lecture Notes

Complete theory notes for all five units. Lab experiments are listed separately below and are assessed through the lab record, not through these notes.

Unit I 7 lecture hours

Simulation Basics

Systems, models and the simulation life cycle; stepped and event-based time advance; discrete versus continuous modelling; numerical techniques, stability and stiffness; sources and propagation of error; stochastic modelling; optimization in simulation models; hybrid and multi-scale modelling; M&S standards; simulation software and tools; ethical and practical considerations.

Simulation clockEuler & RK4Error propagationHLA / DEVS / FMIEthics

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Unit II 7 lecture hours

Dynamical, Finite State and Complex Model Simulations

Graph and network transition simulations; actor-based simulations; mesh-based simulations; hybrid simulations; agent-based and multi-agent simulations; cellular automata; Monte Carlo and probabilistic simulations; event-driven simulation architectures; complex adaptive systems and interdependent critical infrastructure; domain-specific applications.

Epidemic thresholdActorsFDM / FVM / FEMEmergenceGame of LifeMCMC

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Unit III 7 lecture hours

Converting to Parallel and Distributed Simulations

Partitioning the data and the algorithms; handling inter-partition dependencies and the causality constraint; conservative and optimistic synchronisation; dynamic partitioning and load balancing; communication patterns; scalability challenges; graph partitioning; hybrid partitioning; fault tolerance; partitioning for emerging architectures.

Amdahl & GustafsonHalo exchangeTime WarpMETISCheckpointing

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Unit IV 5 lecture hours

Probability and Statistics for Simulations and Analysis

Input distributions and fitting; queueing systems, Kendall's notation, Little's law, M/M/1 and Pollaczek–Khinchine; random noise and pseudo-random number generation; random variate generation by inverse transform, acceptance–rejection and special methods; output analysis with replications and batch means; local and global sensitivity analysis.

Little's lawInverse transformBox–MullerConfidence intervalsSobol indices

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Unit V 4 lecture hours

Simulation Results Analysis and Viewing Tools

From raw output to reportable results; display forms — tables, graphs and multidimensional visualization; scientific and in-situ visualization; terminal, X Window, MS Windows and web interfaces for simulation tools; verification, validation and accreditation of model results, with error metrics and the credibility checklist.

Honest graphicsParallel coordinatesX11 client–serverWeb dashboardsVV&A

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Syllabus Map

Every syllabus item, and the section of the notes where it is covered.

Unit	Syllabus topics	Covered in
I — Simulation Basics	Handling stepped and event-based time; discrete versus continuous modelling; numerical techniques; sources and propagation of error; stochastic modelling and simulation; optimization in simulation models; hybrid and multi-scale modelling; modelling and simulation standards; simulation software and tools; ethical and practical considerations.	Unit I notes, Sections 1–11
II — Dynamical, Finite State and Complex Models	Graph or network transitions based simulations; actor based; mesh based; hybrid simulations; agent-based and multi-agent; cellular automata; Monte Carlo and probabilistic; event-driven simulation architectures; complex adaptive systems; domain-specific applications.	Unit II notes, Sections 1–10
III — Parallel and Distributed	Partitioning the data; partitioning the algorithms; inter-partition dependencies; dynamic partitioning; communication patterns; scalability challenges; partitioning in graph-based systems; hybrid partitioning; fault tolerance; partitioning for emerging architectures.	Unit III notes, Sections 1–10
IV — Probability and Statistics	Introduction to queues and random noise; random variates generation; sensitivity analysis.	Unit IV notes, Sections 2–5
V — Results Analysis and Viewing Tools	Display forms: tables, graphs and multidimensional visualization; terminals, X and MS Windows, and web interfaces; validation of model results.	Unit V notes, Sections 2–5

Examination Scheme (Theory)

Internal Assessment — 50%
Mid Semester — 20%
End Semester — 30%
Modes of evaluation: quiz, assignment, presentation, extempore and written examination.

Examination Scheme (Lab)

Quiz & viva — 50%
Performance & lab report — 50%
Continuous assessment across the ten experiments.

CO Coverage

CO1 — Units I, II, IV
CO2 — Units I, III
CO3 — Units II, III
CO4 — Units IV, V

Laboratory Experiments

Ten experiments, 30 lab hours. Listed here for reference — the notes above are theory only; each experiment names the theory section that supports it.

#	Experiment	Supporting theory
1	Handling stepped and event-based time in simulations (traffic-signal management)	Unit I §2
2	Discrete versus continuous modelling (population growth)	Unit I §3–4
3	Stochastic modelling and simulation (random walks, price fluctuations)	Unit I §6, Unit IV §3–4
4	Graph or network transitions-based simulations (disease spread on a network)	Unit II §1
5	Cellular automata-based simulations (forest-fire propagation)	Unit II §6
6	Monte Carlo and probabilistic simulations (estimating π)	Unit II §7
7	Parallel and distributed simulations (partitioning, speedup, scalability)	Unit III §1–6
8	Random variates generation and sensitivity analysis (queueing models)	Unit IV §2, §4–5
9	Simulation results analysis and validation (traffic flow vs. benchmarks)	Unit V §2, §5
10	Building web interfaces for simulations (parameter input and result views)	Unit V §4

Textbooks and References

As prescribed in the syllabus, plus the standard works cited in the unit notes.

Textbook FACTS: Modelling and Simulation in Power Networks Acha, E., Fuerte-Esquivel, C. R., Ambriz-Pérez, H., and Angeles-Camacho, C. (2004), John Wiley and Sons.

Reference Seminal Contributions to Modelling and Simulation: 30 Years of the European Council of Modelling and Simulation Al-Begain, K., and Bargiela, A., Eds. (2016), Springer.

Reference Spatial Agent-Based Simulation Modeling in Public Health Arifin, S. N., Madey, G. R., and Collins, F. H. (2016), John Wiley and Sons. — supports Unit II.

Reference Stochastic Simulation: Algorithms and Analysis Asmussen, S., and Glynn, P. W. (2007), Springer. — supports Units I, II and IV.

Reference The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration Axelrod, R. M. (1997), Princeton University Press. — supports Unit II.

Reference Handbook of Simulation: Principles, Methodology, Advances, Applications and Practice Banks, J., Ed. (1998), John Wiley & Sons. — supports every unit.

Cited in notes Simulation Modeling and Analysis Law, A. M. — input modelling, output analysis, verification and validation (Units I, IV, V).

Cited in notes Parallel and Distributed Simulation Systems Fujimoto, R. M., Wiley — the standard text for Unit III.

Cited in notes Global Sensitivity Analysis: The Primer Saltelli, A. et al. — Unit IV, Section 5.

These notes are prepared for classroom teaching and semester examination preparation. They summarise and organise material from the sources above; read the originals for depth, and always cite them, not these notes, in written work.