Computational Modelling Group

Artificial Neural Networks

For queries about this topic, contact Richard Watson.

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Projects

Amorphous Computation, Random Graphs and Complex Biological Networks

Seth Bullock (Investigator)

This interdisciplinary research collaboration arose within the Simple Models of Complex Networks research cluster funded by the EPSRC www.epsrca.ac.uk through the Novel Computation Initiative. Here, leading groups from the Universities of Leeds, Sheffield, Nottingham, Southampton, Royal Holloway and King’s College and industrial partners BT are brought together for the first time to develop novel amorphous computation methods based on the theory of random graphs.

Network Analysis of Roman Transport Routes in the Imperial Roman Mediterranean

David Potts

This research is designed to explore the nature of the relationships between Portus, Rome, and other selected ports in the Mediterranean and to establish patterns and the changing nature of trading networks derived from the distribution of known Roman artefacts.

Prediction of orifice flow flooding rates through generic orifices

Dominic Hudson, Ming-yi Tan (Investigators), Christian Wood, Adam Sobey

This presearch concentrates on the modelling of compartment flooding rates following the occurrence of damage in a ship's side shell. Typical state of the art flooding models use Torricelli’s formula to calculate flooding rates using a constant co-efficient of discharge (Cd). Based on Bernoulli’s theorem, turbulence and viscosity effects are not included using a Cd independent of damage shape or size. Previous work indicates that this assumption over-simplifies the problem to an extent where the flooding rates used for calculation are in error. This project will use CFD validated by experiment to calculate flooding rates for a large number of cases from which a 'krigged' response surface will be generated. Validity of the subsequent response surface will be interrogated.

Spatially Embedded Complex Systems Engineering

Seth Bullock (Investigator)

SECSE brought together an interdisciplinary team of scientists working on an ambitious three-and-a-half year project titled. The research cluster spanned neuroscience, artificial intelligence, geography, and complex systems in an attempt to understand the role of spatial organization and spatial processes in complex networks within the domains of neural control, geo-information systems and distributed IT systems such as those implicated in air-traffic control.

People

Seth Bullock
Professor, Electronics and Computer Science (FPAS)
Andrew Collins
Professor, Medicine (FM)
Dominic Hudson
Senior Lecturer, Engineering Sciences (FEE)
Denis Kramer
Lecturer, Engineering Sciences (FEE)
Paul Skipp
Lecturer, Biological Sciences (FNES)
Ming-yi Tan
Lecturer, Engineering Sciences (FEE)
Thomas Blumensath
Senior Research Fellow, Institute of Sound & Vibration Research (FEE)
Jordi Arranz
Postgraduate Research Student, Electronics and Computer Science (FPAS)
Leonidas Bourikas
Postgraduate Research Student, Civil Engineering & the Environment (FEE)
Alicia Costalago Meruelo
Postgraduate Research Student, University of Southampton
Jonathan Gray
Postgraduate Research Student, Electronics and Computer Science (FPAS)
Tom Hebbron
Postgraduate Research Student, Electronics and Computer Science (FPAS)
Joshua Jeeson Daniel
Postgraduate Research Student, Engineering Sciences (FEE)
Alkin Nasuf
Postgraduate Research Student, Engineering Sciences (FEE)
Lyuboslav Petrov
Postgraduate Research Student, Electronics and Computer Science (FPAS)
David Potts
Postgraduate Research Student, Humanities (FH)
Alvaro Ruiz-Serrano
Postgraduate Research Student, Chemistry (FNES)
Adam Sobey
Postgraduate Research Student, Engineering Sciences (FEE)
Pegah Tayaranian Hosseini
Postgraduate Research Student, Institute of Sound & Vibration Research (FEE)
Christian Wood
Postgraduate Research Student, Engineering Sciences (FEE)
Petrina Butler
Administrative Staff, Research and Innovation Services
Daniel Pope
External Member, Mauve Internet Ltd.
Doroteya Staykova
None, None