What is Simulation Science?

Simulation Science is a rapidly emerging field involving the collaboration of applied mathematicians, computer scientists and researchers from many areas of applied science. Simulation Science uses the techniques of Applied Mathematics and Computer Science for the development of problem solving methodologies and robust application tools. The techniques are used in many application areas, including science, engineering and finance/economics.

Many scientists see Simulation Science as a fundamental new mode of scientific inquiry taking its place alongside and complementing theoretical and experimental science. The complexity of modern industrial problems and the growing access to affordable high performance computing facilities has generated a demand for scientists and engineers who possess the sophisticated computational skills required to tackle these problems. The Programme in Simulation Science is a response to this demand. These courses are strongly interdisciplinary, emphasising modern computational methods and geared to providing both training of new graduates and through the modular structure of the programme the retraining of professional scientists and engineers currently employed in industry.

The programme is run jointly by the School of Computer Science and Informatics and the School of Mathematical Sciences.

Simulation Science Examples

Techniques from simulation science are used to solve engineering problems such as heat diffusion and fluid dynamics.

Diffusion Simulation

Simulation of Drug Dissolution

They can also be applied to pharmaceutical problems, such as the development of drug delivery systems; economics and financial problems such as the pricing of stock options; or operations management problems such as planning and scheduling.

Diffusion Simulation

Partitioning of a Finite Element problem for simulation on an 8-processor parallel architecture

Many simulation science simulation techniques are resource-intensive and require the efficient use of computer architectures such as multi-processor parallel machines. Modern environments for simulation science include computational Grids that harness geographically distributed resources over the Internet.