Computational and Data Science for Fusion - PHY00059M

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Module summary

At the heart of fusion is the need to manage vast projects that generate copious amounts of data that requires analysis, and a dependence upon numerical calculations for modelling fusion plasmas and machines. Computational tools are employed to understand the things we cannot measure or have not yet built. This is a rapidly evolving subject, and harnessing its potential is central to moving fusion forward faster. For this reason, we all need a good grasp of the opportunities computational and data science affords, what they involve and an awareness of how these tools are being deployed.

This module is suitable for all students including those who are new to programming. It builds upon the knowledge and skills introduced in Semester 1 via the core modules. Python programming skills will be introduced or further developed alongside learning about a variety of fusion-relevant data analysis and modelling techniques. The module will highlight the opportunities in data and computational science whilst identifying some of the pros and cons of the various methodologies.

Related modules

Pre-requisites: Fusion: from Concepts to Technologies and Professional & Technical Skills in Fusion

Module will run

Occurrence	Teaching period
A	Semester 2 2026-27

Module aims

Subject content aims:

Module content will be delivered across three themes:

• Theme 1: Data science and machine learning.
  this involves collecting, labelling, analysing, and interpreting data to identify trends and inconsistencies, leading to data-driven predictions and decision-making.

• Theme 2: Simulation and modelling of fusion systems.
  this involves physics and engineering intensive calculations that solve a series of equations to enable the prediction of how a fusion plasma or integrated fusion machine might behave.

• Theme 3: Working with experimental data.
  this involves extracting information from measurements, which are often incomplete and subject to uncertainty or error, in order to understand the findings of an experiment. Using measurement, often combined with simulation and data science, we aim to identify relationships between variables and improve our models.

Graduate skills aims:

• Be able to discuss and communicate across a knowledgeable yet diverse group which software approach is needed to address a problem.

• Identify opportunities for the novel use of computational and data analysis tools.

Module learning outcomes

Subject content learning outcomes
After successful completion of this module, students will be able to:

• Articulate the importance of computational and data science across fusion, and articulate which numerical methods are appropriate for different tasks within fusion whilst evaluating the limitations.

• Create a program in a language such as Python and use this to apply numerical methods in solving a range of problems representative of those found in fusion science and technology.

• Analyse and evaluate fusion data to extract information and uncertainties associated with this information.

Graduate skills learning outcomes
After successful completion of this module, students will be able to:

• Use relevant software tools and systems.

• Write and document Python scripts.

• Use cloud-based version control software to maintain personal contemporary and shared records of the development of simulations, computer programs (e.g. Python) and other documentation.

• Demonstrate how to handle incomplete information and uncertainties whilst drawing a piece of work to a conclusion.

Indicative assessment

Task	% of module mark
Essay/coursework	50.0
Essay/coursework	50.0

Special assessment rules

None

Indicative reassessment

Task	% of module mark
Essay/coursework	50.0
Essay/coursework	50.0

Module feedback

Marks for all summative assessments will be made available to you and your supervisor via e:vision.

You may receive formative feedback that may be at a whole class or individual level. Progress of the module can be discussed with the module leader or GTA and your supervisor.

Indicative reading

E. Smith: Introduction to the Tools of Scientific Computing, Springer 2020