Core modules
Fundamentals of Predictive Modelling
This module provides students with fundamental knowledge for predictive modelling and uncertainty quantification. It gives an overview of the essential elements of the mathematical, statistical, and computational techniques needed to provide well-calibrated predictions for the behaviour of physical systems.
Numerical Algorithms and Optimisation
This module provides students with knowledge (and practice) of important numerical optimisation concepts at the intersection between mathematics and scientific computing. Algorithmic structures, data structures, numerical method construction and performance assessment will form key parts of the module, with applications and use cases concentrated on topics in linear algebra, signal processing and optimisation.
Scientific Machine Learning
This module provides students with knowledge in the modern field of scientific machine learning, which is a fusion of scientific computing and machine learning. Students will learn how to use a variety of statistical and machine learning techniques to train models which combine data-driven and mechanistic models and assess their ability to make useful predictions.
Predictive Modelling Group Project
Groups of students will create a complex piece of predictive modelling research software using methods and design principles introduced in previous modules in the course. They will also deliver written and oral reports of their project. In this module, students also receive training in key professional and research skills, including collaborative writing.
Individual Research Project
Each student will conduct significant and novel research as an individual project and present the background and findings in the form of a dissertation. The research question must address some aspect of modelling, resulting in new knowledge, methodology or understanding, accompanied by uncertainty quantification.
Optional modules
Previously, a selection of the following options has been offered:
- Modelling and Computation of Fluid Dynamics Across Phases and Scales *
- Particle-based Modelling *
- Predictive Modelling of Advanced Engineering Materials*
- High Performance Computing
- Statistical Learning and Big Data
- Advanced Topics in Fluids
- Advanced Computational Chemistry
- Biomolecular Simulation
- Biomedical Systems Modelling
*Please note that students are required to take at least one of these three optional modules.
PG Diploma, Certificate and Award options are also available for those who would like to take a subset of modules. More information
