2016 SIAM Conference on Mathematics of Planet Earth

The mpe16-logopurpose of Mathematics of Planet Earth (SIAG/MPE) activity group is to provide a forum for mathematicians and computational scientists to study Planet Earth, its life-supporting capacity, and the impact of human activities. By opening up a new area of applications, the stimulates interesting research in the mathematical sciences. Activities of the SIAG  include the 2016 SIAM Conference on Mathematics of Planet Earth (MPE16), which took place in Philadelphia, between  September 30th and October 2nd and where the MPE CDT students had a strong presence.

The range and diversity of the opportunities for applications of Mathematics of Planet Earth was illustrated by the plethora of research advances showcased at MPE16. The conference  included a mini-symposium which highlighted some of the research activities in the EPSRC Centre for Doctoral Training in Mathematics of Planet Earth (MPE CDT).


Details of the mini-symposium were as follows:
Organizer: Darryl D. Holm (MPE CDT staff member), Imperial College of London, United Kingdom

2:15-2:40 An Introduction to Multilevel Monte Carlo Methods for Uncertainty Quantification in Earth Science abstract, Tobias Schwedes (MPE CDT studnet), Imperial College, United Kingdom

2:45-3:10 Forward-Backward Stochastic Differential Equations: Applications to Carbon Emissions Markets abstract, Hinesh Chotai (MPE CDT studnet), Imperial College, United Kingdom

3:15-3:40 Stochastic and Statistical Modelling of Extreme Meteorological Events: Tropical Cyclones abstract, Thomas P. Leahy (MPE CDT studnet), Imperial College, United Kingdom

3:45-4:10 Mimetic Discontinuous Galerkin Methods for Simulation of Nonlinear Wave Interactions abstract, James Jackaman (MPE CDT studnet), University of Reading, United Kingdom

siam-tobias siam-thomassiam-hineshIn addition to that, on Sunday October 2 Paulina Rowinska, another the MPE CDT student, was presenting during the Simulation and Analysis Session. Paulina delivered a talk on “Bayesian Inference for Expensive Simulators”.