Study information

Reduced Order Modelling and Model Discovery

Module titleReduced Order Modelling and Model Discovery
Module codeENSM007
Academic year2025/6
Credits15
Module staff

Module description

The study of complex systems requires solving large numerical simulations, such as partial differential equations, repeatedly or in real time. Despite advanced computational resources, this remains a significant challenge. Many complex systems exhibit dominant low-dimensional patterns in the data, which can be leveraged to create reduced order models that are faster to evaluate while accurately reproducing the original system. This module covers data-driven techniques for reduced order modelling and model discovery, with applications in design, prediction, uncertainty quantification, and control. You will learn state-of-the-art mathematical methods and machine learning models to address current challenges in science and engineering. Topics include turbulent flows, biological systems, climate, epidemiology, finance, and robotics.

Module aims - intentions of the module

Intended Learning Outcomes (ILOs)

ILO: Module-specific skills

On successfully completing the module you will be able to...

    ILO: Discipline-specific skills

    On successfully completing the module you will be able to...

      ILO: Personal and key skills

      On successfully completing the module you will be able to...

        Summative assessment (% of credit)

        CourseworkWritten examsPractical exams
        000
        Credit value15
        Module ECTS

        7.5

        NQF level (module)

        7

        Available as distance learning?

        No

        Origin date

        26/09/2024

        Last revision date

        26/09/2024