About the event
Organiser: Professor Benedict Leimkuhler (The Alan Turing Institute, UK)
Day 1: 11:00 - 18:00
Day 2: 10:00 - 18:00
Day 3: 10:00 - 17:00
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.
Assyr Abdulle (EPFL)Probabilistic numerical methods and Bayesian multiscale inverse problems
José Carrillo de la Plata (Imperial)Consensus based models and applications to global optimization
Vincent Danos (ENS)Stability and inference for position-dependent Langevin diffusions
Pierre Degond (Imperial)From micro to macro in collective dynamics
Martin Hairer (Imperial)Universality classes for 1+1 dimensional systems
Jane Hillston (Edinburgh)Moment analysis, model reduction and the London Bike Sharing Scheme
Heinz Koeppl (Darmstadt)Biomolecular reaction networks in random environments
Tony Lelievre (Paris)Coarse-graining of stochastic dynamics: Markov state models and effective reduced dynamics
Christian Maes (Leuven)Pattern formation in nonequilibrium media
Jonathan Mattingly (Duke)Discovering the geopolitical structure of the United States through Markov Chain Monte Carlo sampling
Greg Pavliotis (Imperial)Data-driven coarse-graining
Sebastian Reich (Potsdam)Learning models by making them interact
Bruce Turkington (U. Mass)Statistical-dynamical models that minimize the rate of information loss
Jonathan Weare (Chicago)Stratification for Markov Chain Monte Carlo and cosmological parameter estimation
Eric Vanden-Eijnden (NYU Courant Institute)Uncertainty quantification, large deviations and rogue waves
Marie-Therese Wolfram (Warwick)Parameter identification for nonlinear partial differential equations in crowd modelling
There is an opportunity to propose a poster for presentation at the meeting. Registration is a two-stage process.