Introductory description

Particle-based simulations are used to study processes from the microscopic to astronomic scales. Examples include atomic and colloidal systems, biological cells, epidemiology, crowds, self-driving cars or astronomical objects. This module will introduce the statistical mechanics foundations of modelling, and will cover methods such as molecular dynamics, Monte Carlo and lattice based simulations. Problem focussed workshop sessions will illustrate the use of software tools for specific groups of models. Case studies by guest lecturers will provide further insight into applications.

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

This module will introduce particle based simulation for students studying for the MSc in Predictive Modelling. A theoretical discussion of the statistical mechanics basics will be followed by applications of these principles in different domains, with examples from guest lecturers changing from year to year and including topics such as:

• atomistic simulations for materials and molecular modelling
• lattice models, such as the Ising model to illustrate statistical physics concepts
• coarse grain models of colloidal particles or biological cells
• simulations for active matter, such as flocking or crowd dynamics
• agent based modelling, such as epidemiology or self-driving cars
• astronomical modelling.

Learning outcomes

By the end of the module, students should be able to:

• Interpret the statistical mechanics of particles
• Demonstrate substantial familiarity with application areas of particle-based simulations
• Develop software tools to model assemblies of entities
• Evaluate the uncertainty of simulation results
• Design computational experiments to predict quantities of interest

Indicative reading list

Research articles in the field will complement the books.

View reading list on Talis Aspire

Subject specific skills

To understand statistical mechanics and its applications in different scientific and engineering domains.
To design, perform and analyse simulation experiments, quantifying the uncertainty in the results.
To appreciate capabilities and limitations of simulation studies.

Transferable skills

Ability to work with diverse software environments.
Ability to create automated, reproducible and robust workflows.
Problem solving, logical reasoning.