PROGRAMME / 02
Bachelor · Artificial Intelligence and Numerical Modelling
Use applied mathematics, scientific computing and AI to build, solve and optimise models for complex scientific and engineering problems.
Progress from foundations to professional practice. Planned workload: 180 credits.
YEAR 01Mathematics and Scientific Programming
- Common Computing Foundations
- Calculus
- Linear Algebra
- Scientific Computing with Python
- Probability and Statistics
- Scientific Modelling Foundation Project
YEAR 02Numerical Methods and System Representation
- Ordinary Differential Equations
- Numerical Linear Algebra
- Numerical Computing
- Optimisation Methods
- Machine Learning
- Fundamental Physics
YEAR 03Intelligent Modelling and Solvers
- Introduction to Partial Differential Equations
- Finite Difference and Finite Element Methods
- Parameter Estimation and Inverse Problems
- Scientific Machine Learning
- Error and Uncertainty
- Modelling Capstone
SHARED LEARNING
Shared foundations. Specialist depth.
Share AI, optimisation and parallel computing modules, with dynamical systems and model validation shared with the digital twins pathway.
Specialist focus: Mathematical formulation, numerical solutions, error analysis and scientific machine learning.
Assessment and outcomes
Evaluate assumptions, accuracy, stability and efficiency. Validate against analytical solutions, benchmarks, experimental data or independent methods, and explain the limits of the model.
Develop a scientific or engineering model, solve and calibrate it, analyse errors, and deliver executable code with a validation report.