17:00
17:00
15:45
14:15
16:30
The paradoxical behaviour of rolling bodies
Abstract
Why does a spinning coin come to such a sudden stop? Why does a
spinning hard-boiled egg stand up on its end? And why does the
rattleback rotate happily in one direction but not in the other?
The key mathematical aspects of these familiar dynamical phenomena,
which admit simple table-top demonstration, will be revealed.
14:30
From Complexity to order: modelling the social behaviour of cells
14:15
Transforms of time and complex space - and their applications to option pricing
14:15
Large mixing angles for neutrinos from infrared fixed points
(Dennis Sciama Lecture Theatre of NAPL)
16:30
16:15
14:30
PLEASE NOTE THERE WILL BE A JOINT SEMINAR WITH NUMERICAL ANALYSIS
Computation of highly-oscillatory problems made easy
Computation of highly-oscillatory problems made easy
Abstract
Rapidly oscillating problems, whether differential equations or
integrals, ubiquitous in applications, are allegedly difficult to
compute. In this talk we will endeavour to persuade the audience that
this is false: high oscillation, properly understood, is good for
computation! We describe methods for differential equations, based on
Magnus and Neumann expansions of modified systems, whose efficacy
improves in the presence of high oscillation. Likewise, we analyse
generalised Filon quadrature methods, showing that also their error
sharply decreases as the oscillation becomes more rapid.
15:00
A solution to the tennis ball problem (using the Tutte polynomial)
17:00
A variational approach to optimal design
14:15
16:30
16:30
Fitting stochastic models to partially observed dynamics
Abstract
In many applications of interest, such as the conformational
dynamics of molecules, large deterministic systems can exhibit
stochastic behaviour in a relative small number of coarse-grained
variables. This kind of dimension reduction, from a large deterministic
system to a smaller stochastic one, can be very useful in understanding
the problem. Whilst the subject of statistical mechanics provides
a wealth of explicit examples where stochastic models for coarse
variables can be found analytically, it is frequently the case
that applications of interest are not amenable to analytic
dimension reduction. It is hence of interest to pursue computational
algorithms for such dimension reduction. This talk will be devoted
to describing recent work on parameter estimation aimed at
problems arising in this context.
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Joint work with Raz Kupferman (Jerusalem) and Petter Wiberg (Warwick)