Home Up Reach Us Contents                             

Complex Systems  

Smart Buildings Complex Systems


Integrated Analysis of System Of Systems

horizontal rule


Our work focuses on exploring the properties of complex systems, and defining inference algorithms that can perform various types of inference on complex systems by exploiting the properties of the complex systems and/or employing approximation techniques.



Properties of complex systems: recent research has shown that, at a gross level, all complex systems have a topology that can be described using a graphical representation called a real-world graph, examples of which are scale-free and small-world graphs. However, we have shown that real-world graphs are too simple to model the details of actual complex systems, and one has to define the topological model for a complex system in terms of an optimization problem, in which we optimize the systemís structure with respect to a particular functionality. In particular, we have explored the optimization problem underlying designing circuits that are easily diagnosable, and are exploring the optimization problem underlying biological systems and various biological functions.



Models for complex systems: many different



Inference algorithms for complex systems: we are developing techniques for performing inference efficiently on large, complex systems using compilation techniques and approximation methods. Compilation refers to pre-computing the complex system to generate a compiled representation such that embedded inference is at worst linear in the size of the embedded representation. For many interesting tasks, such as control, diagnosis and estimation, the size of the embedded representation is exponential in the size of the original system parameters, so we focus on generating compact compiled representations or compact approximate compiled representations. We are also studying a variety of other approximation methods, such as local search and Monte-Carlo sampling approaches.


Focus Areas


Modeling Complex Systems; Automated Model Generators

bulletStochastic algorithms for complex systems inference
bulletApproximation algorithms for complex systems inference
bulletApplication areas: systems diagnosis, control, bio-applications


bulletDirector: Prof. G. Provan
bulletPhD students: Jun Wang, Margarita Razgon
bulletPostdoctoral fellows: Olga Tveretina, Igor Razgon, Damien Woods, Menouer Boubekeur


Publications generated under this project are described here.



This project is funded by Science Foundation Ireland



Send mail to ccsl@cs.ucc.ie with questions or comments about this web site.
Copyright © 2008 Cork Complex Systems Lab
Last modified: 05/26/08