Events

May
29
Fri 2015

14:00 - 17:00

Organised by Sam Blackburn.

Hosted by University of Birmingham.

May
18
Mon 2015

14:00 - 17:00

Organised by Sam Blackburn.

Hosted by University of Birmingham.

May
5
Tue 2015

16:00 - 17:00

Hosted by University of Exeter.

Feb
9
Mon 2015

14:00 - 16:30

Hosted by University of Exeter.

Feb
2
Mon 2015

14:00 - 15:00

Organised by Prof Peter Ashwin.

Hosted by University of Exeter.

Jan
12
Mon 2015

14:00 - 15:00

Organised by Dr Giampaolo D'Alessandro.

MAGIC Career Fair
Monday 12th January 2015 at 2pm
MAGIC Public room
Confirmed panelists:
Richard Pinch (GHCQ) and Richard Hill (Atass Sports)

This is a QuestIon and Answer session on jobs opportuniIes aimed specifically at mathematics and statistics PhD students. If you are considering looking for a job this will be an ideal occasion to hear from the panelists what skills they require and to ask them quesIons on how best to approach the job market.

How to attend
To attend the MAGIC Career Fair use the Visimeet client to join the MAGIC Public room (meeting ID 55993743) or press the “Connect” button on the MAGIC web site.

Dec
1
Mon 2014

16:00 - 17:00

Hosted by University of Surrey.

http://www1.maths.leeds.ac.uk/~rsturman/ag_dynamics_seminar/wulff.html

Oct
9
Thu 2014

15:00 - 16:00

Organised by Dr Giampaolo D'Alessandro.

Hosted by University of Reading.

MAGIC annual lecture 2014/15

Noether's theorem 100 years on

Professor Elizabeth Mansfield
University of Kent


In 1918, Emmy Noether showed how to calculate conserved quantities for variational problems which have a Lie symmetry, such as invariance under translations and rotations in time and space. Noether obtained the complete result, including the case of invariance under pseudo group actions.

In this talk I will show how Noether's two theorems have been updated to finite difference and to numerical schemes. One outstanding motivation is the desire to incorporate a particular conservation law, potential vorticity, into numerical schemes, needed (I am told) for extreme weather prediction.

Access Grid Dynamics Seminar: Network Models of Visual Illusions and Rivalry

Jun
9
Mon 2014

16:00 - 17:00

Hosted by University of Exeter.

In binocular rivalry, conflicting images are presented to the two eyes, and the visual system interprets this combination in sometimes surprising ways. Visual illusions involve ambiguous or incomplete information, presented simultaneously to both eyes. Well-known illusions include the Necker cube, the rabbit/duck illusion, the cartoonist William Ely Hill's 'my wife and my mother-in-law', and the spinning dancer, in which a moving image of a dancer appears to spin in either the clockwise or anticlockwise direction.
In 2009 Hugh Wilson proposed a neural network model for high-level decision-making in the brain, based on the phenomenon of binocular rivalry. Diekman, Golubitksy and Wang observed that Wilson networks are useful for understanding rivalry itself. The talk describes ongoing work with Golubitsky and Diekman in which we generalise Wilson networks, and model illusions as well as rivalry. The model corresponds well to several experiments in the literature, and in some cases leads to new predictions.

CliMathNet Seminar: Statistical emulation as a model-coupling tool for integrated assessments of climate change

Jun
5
Thu 2014

16:00 - 17:00

Hosted by *External.

CliMathNet e-seminar: Hamilton's Principle for the GFD Model Hierarchy

Apr
29
Tue 2014

15:00 - 16:00

Hosted by University of Exeter.

Applying asymptotics, averaging and reduction by symmetry in Hamilton's principle for the Euler fluid equations governing a rotating, stratifi ed incompressible flow produces the main sequence of GFD approximations. Each model in this sequence of approximations possesses a Kelvin circulation theorem, and conserves energy and potential vorticity (PV). Legendre transforming the symmetry-reduced Lagrangian yields the Lie-Poisson Hamiltonian formulation of GFD and its Eulerian conservation laws, which may be used to classify steady solutions as relative equilibria and determine sufficient conditions for their nonlinear stability.

AG Dynamics Seminar, Title TBA

Mar
26
Wed 2014

14:00 - 15:00

Hosted by University of Liverpool.

CliMathNet e-seminar: Accounting for model error due to unresolved scales within ensemble Kalman filtering

Mar
25
Tue 2014

15:00 - 16:00

Organised by Prof Peter Ashwin.

Hosted by *External.

We propose a method for accounting for model error due to unresolved scales within the context of ensemble Kalman filtering. This method estimates a model error correction to the forecast step by using historical reanalysis increments to build a model error covariance matrix. We compare two different versions of the method; a time-constant model error treatment where the same model error bias correction is added after each forecast, and a time- varying treatment where the bias correction randomly varies with each forecast. We compare both methods with the standard method of dealing with model error through inflation and localization, and illustrate our results with numerical simulations on a low order nonlinear system showing chaotic dynamics. The results show that the filter skill is significantly improved through the proposed model error treatments, and that both methods require far less parameter tuning than the standard approach. This is joint work with Alberto Carrassi.

CliMathNet seminar: Spatial methods that combine ideas from wavelets and lattices

Nov
14
Thu 2013

15:00 - 16:00

Hosted by University of Exeter.

Kriging is a non-parametric regression method used in geostatistics for estimating curves and surfaces and forms the core of most statistical methods for spatial data. In climate science these methods are very useful for estimating how climate varies over a geographic region when the observational data is sparse or the computer model runs are limited. A statistical challenge is to implement spatial methods for large sample sizes, a common feature of many geophysical problems. Here a new family of covariance models is proposed that expands the field in a set of basis functions and places a Gaussian Markov random field (GMRF) latent model on the basis coefficients. The idea, in contrast to fixed rank Kriging, is to use many basis functions organized on lattices. In addition, the basis functions add more smoothness and larger scale spatial dependence that a GMRF alone. The computational efficiency arises because one can choose basis functions with compact support and also a precision matrix for the GMRF that is very sparse. The impact is that evaluating the model likelihood and computing spatial predictions is feasible even for tens of thousands of spatial observations. Moreover, by the varying the support of the basis functions and the correlations among basis coefficients it is possible to entertain multi-resolution and nonstationary spatial models. A practical example is also presented for a subset of the North American Regional Climate Change and Assessment Program model data. Here fields on the order 10^4 observations are compared within the R data analysis environment.

Access Grid Dynamics Seminar: Title TBC

Oct
29
Tue 2013

16:00 - 17:00

Hosted by *External.

A