MEng Aerospace Engineering / Course details

Year of entry: 2024

Course unit details:
Data-driven Modelling & Simulation

Course unit fact file
Unit code AERO40041
Credit rating 15
Unit level Level 4
Teaching period(s) Semester 1
Available as a free choice unit? No


Data science and machine learning have become more and more widely used in research and engineering firms and so are essential skills for a graduate engineer. This unit introduces students to advanced modelling and simulation concepts such as machine learning, data-driven optimisation, surrogate modelling and uncertainty quantification, relevant to modern design in Aerospace and Mechanical Engineering. With the help of hands-on laboratories for solving Engineering problems, the unit will build the capabilities of data-driven modelling that graduates should expect to use in their careers. 



Unit title Unit code Requirement type Description
Modelling & Simulation 3 AERO30052 Pre-Requisite Compulsory


To lay the foundations of data science and machine learning for the practical solutions to engineering problems. You will expand upon prior knowledge covered in Modelling and Simulation and the Numerical Methods and Computing courses through the use of modern data-driven methods for engineering problems. You will apply this knowledge to different engineering goals. Central to this is the development of both the theoretical and computational aspects of applied data science, data-driven optimisation, machine learning and uncertainty quantification. The skills developed will be honed via hands-on laboratories aimed at solving complex engineering problems.


An introduction to machine learning for engineering analysis -- the motivation, notation and definitions. Fundamental Algorithms: Linear regression, KNN algorithm, Gradient decent, Neural Networks for regression tasks in engineering. Overview of optimization, terminologies and methods; Sensitivity-based optimization; Grid-search and surrogate-based optimizations; Gaussian process regression for surrogate construction; Bayesian optimization based on Gaussian processes. Cross-validation and over-fitting; An overview to uncertainty quantification. A high-level overview of advanced AI (artificial intelligence) models for engineering analysis; reinforcement learning and generative AI. 

Practical skills


Assessment methods

Method Weight
Written exam 50%
Report 50%

Feedback methods

Each piece of coursework is supported by timetabled computer sessions. The students will be expected to attempt the coursework in their own time and ask for feedback from teaching staff during the computer sessions, before submission. T

Study hours

Scheduled activity hours
Lectures 24
Practical classes & workshops 12
Independent study hours
Independent study 114

Teaching staff

Staff member Role
Lee Margetts Unit coordinator

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