The Scientific Computing Group is a multidisciplinary group lead by Professor Wes Armour.

Our focus is on the application of Scientific Computing and many-core parallel algorithms in a wide range of problems. The group has worked in protein crystallography, condensed matter, theoretical particle physics, radio astronomy, micro tomography, signal processing and computer science. We are committed to and strongly supports interdisciplinary research.

Strong emphasis is also put on the use of Machine learning and Neural Networks. Previous research projects included Neural Nets to generate image specific wavelets to enhance image compression as part of the JPEG2000 compression scheme, the use of neural networks to increase the accuracy of Finite Element Models (FEM) of offshore wind turbine foundations, at the AI/HPC intersection we used NN and decision trees to optimise real-time GPU codes, we have used Convolutional Auto Encoders (CAEs) to remove Radio Frequency Interference (RFI) from radio astronomy data and the use of compressed sensing techniques to enhance images generated by radio telescopes.

Our group is a long term contributor to the Square Kilometre Array (SKA) radio telescope. Previously we had contributed to the SKA by developing a prototype of the GPU accelerated real-time processing pipeline for time-domain radio astronomy data processing. We are continuing to develop new and novel algorithms and software for SKA data processing as part of Scientific Data Processor that design and develops core of the data processing software for the SKA.

The group also delivers the following many-core courses:

• An introduction to HPC and Scientific Computing
• Course on CUDA Programming on NVIDIA GPUs