Program Overview
Modelling and Simulation (COMP0212)
Key Information
The Modelling and Simulation module is part of the Faculty of Engineering Sciences, specifically within the Computer Science department. It carries a credit value of 15.
- Faculty: Faculty of Engineering Sciences
- Teaching department: Computer Science
- Credit value: 15
- Restrictions: Module delivery for UG (FHEQ Level 5) available on MEng Robotics and Artificial Intelligence.
Alternative Credit Options
There are no alternative credit options available for this module.
Description
Building real robotic systems can be expensive and time-consuming, especially for new systems where the investment may not pay off. This module focuses on creating mathematical and computational models of real and realistic robotic systems, analyzing their performance in simulation, and interpreting the results. It is a practical module based on underpinning engineering principles.
Aims
The aims of this module are to:
- Provide students with enabling knowledge to employ practical techniques for designing, building, and assessing and optimizing the performance of real-world robotic and AI systems.
- Support students in problem-solving and creating practical solutions in robotics and AI against functional and non-functional user requirements, testing and assessing those in simulated and real-world environments, and articulating the limitations of those assessments.
- Provide students with the tools for critical analysis, including reasoning about the appropriateness and quality of practical solutions produced in the context of the problems defined.
Intended Learning Outcomes
On successful completion of this module, a student will be able to:
- Design (and justify the design of) a simulation of a cyber-physical system based on a mixture of traces, mathematical modeling, and random components.
- Implement the simulation and then run it to obtain results.
- Justify a process for verifying and validating the simulation, given the results.
- Assess the sensitivity of the simulation to small changes in key parameters.
- Report on the results obtained, reflecting on the strength of the conclusions that can be drawn and/or on the requirements for further data gathering to improve confidence.
Indicative Content
The module will typically cover:
- The role of modeling and simulation: advantages and disadvantages.
- Types of simulation: discrete event, Monte Carlo, agent-based, systems dynamics.
- Available simulation software.
- Basics: random number generation, seeding random number generators.
- Basics: Experimental design.
- Introduction to case study – a 'digital twin' for a CPS.
- The simulation process: Problem definition – define the problem and the performance metrics, System definition – define appropriate levels of abstraction to address the problem.
- Model building and formulation.
- Input modeling: collecting data and fitting to theoretical distributions.
- Model testing, from components to integrated model.
- Model coding.
- Simulation verification and validation.
- Experimentation.
- (Care in) Interpreting and reporting results.
- Hybrid (hardware in the loop) simulation.
- Examples of the simulation of human behavior.
This module also provides an initial understanding of experimental research design, to be further developed in group projects in Year 3 and Individual projects in Year 4.
Requisite Conditions
To be eligible to select this module as optional or elective, a student must be registered on a programme and year of study for which it is formally available.
Module Deliveries for 2026/27 Academic Year
- Intended teaching term: Term 1
- Undergraduate (FHEQ Level 5)
Teaching and Assessment
- Mode of study: In person
- Intended teaching location: UCL East
- Methods of assessment:
- 50% Clinical, laboratory or practical activity (2 assessments)
- 50% Group activity (2 assessments)
- Mark scheme: Numeric Marks
Other Information
- Number of students on module in previous year: 44
- Module leader: Professor Igor Gaponov