Complex models in all areas of science and engineering, and in the social sciences, must be reduced to a relatively small number of variables for practical computation and accurate prediction.
In general, it is difficult to identify and parameterize the crucial features that must be incorporated into the model, but powerful statistical approaches are becoming available based on analysis of large volumes of data. This is bringing fundamental change in the way we think about models. A new modelling paradigm is emerging based on the combination of statistical inference, high-throughput computation and physical laws, yet the mathematical foundations for combining these methods are still in their infancy.
The purpose of this workshop is to bring together a diverse group of mathematicians and computational scientists to explore new ways of incorporating data analysis into complex systems modelling. Application topics to be discussed include methods for collective dynamics (flocking, schooling and pedestrian models), molecular modelling, cell biology, and fluid dynamics.
The programme on each day will consist primarily of invited talks addressing diverse perspectives on data-driven modelling of complex systems. There will also be a poster session, and many opportunities for informal interaction and discussion.
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