Computational fluid dynamics
Abstract
The computation of flows of compressible fluids will be
discussed, exploiting the symmetric form of the equations describing
compressible flow.
The computation of flows of compressible fluids will be
discussed, exploiting the symmetric form of the equations describing
compressible flow.
We shall review recent progress in the understanding of
isoperimetric inequalities for product probability measures (a very tight
description of the concentration of measure phenomeonon). Several extensions
of the classical result for the Gaussian measure were recently derived by
functional analytic methods.
The Hopfield model took his name and its popularity within the theory
of formal neural networks. It was introduced in 1982 to describe and
implement associative memories. In fact, the mathematical model was
already defined, and studied in a simple form by Pastur and Figotin in
an attempt to describe spin-glasses, which are magnetic materials with
singular behaviour at low temperature. This model indeed shows a very
complex structure if considered in a slightly different regime than
the one they studied. In the present talk we will focus on the
fluctuations of the free energy in the high-temperature phase. No
prior knowledge of Statistical mechanics is required to follow the
talk.
The first seminar will be given with the new students in
mind. It will begin with a brief overview of quantifier elimination and its
relation to the back-and-forth property.I shall then discuss complexity issues
with particular reference to algebraically closed fields.In particular,how much
does the height and degree of polynomials in a formula increase when a
quantifier is eliminated? The precise answer here gave rise to the definition
of a `generic' transcendental entire function,which will also be
discussed.
A cell is a wonderously complex object. In this talk I will
give an overview of some of the mathematical frameworks that are needed
in order to make progress to understanding the complex dynamics of a
cell. The talk will consist of a directed random walk through discrete
Markov processes, stochastic differential equations, anomalous diffusion
and fractional differential equations.
Joe Doob, who died recently aged 94, was the last survivor of the
founding fathers of probability. Doob was best known for his work on
martingales, and for his classic book, Stochastic Processes (1953).
The talk will combine an appreciation of Doob's work and legacy with
reminiscences of Doob the man. (I was fortunate to be a colleague of
Doob from 1975-6, and to get to know him well during that year.)
Following Doob's passing, the mantle of greatest living probabilist
descends on the shoulders of Kiyosi Ito (b. 1915), alas now a sick
man.
We are interested in a microscopic stochastic description of a
population of discrete individuals characterized by one adaptive
trait. The population is modeled as a stochastic point process whose
generator captures the probabilistic dynamics over continuous time of
birth, mutation and death, as influenced by each individual's trait
values, and interactions between individuals. An offspring usually
inherits the trait values of her progenitor, except when a mutation
causes the offspring to take an instantaneous mutation step at birth
to new trait values. Once this point process is in place, the quest
for tractable approximations can follow different mathematical paths,
which differ in the normalization they assume (taking limit on
population size , rescaling time) and in the nature of the
corresponding approximation models: integro or integro-differential
equations, superprocesses. In particular cases, we consider the long
time behaviour for the stochastic or deterministic models.
The role of electron emission (either thermionic, secondary or
photoelectric) in charging an object immersed in a plasma is
investigated, both theoretically and numerically.
In fact, recent work [1] has shown how electron emission can
fundamentally affect the shielding potential around the object. In
particular, depending on the physical parameters of the system (that
were chosen such to correspond to common experimental conditions), the
shielding potential can develop an attractive potential well.
The conditions for the formation of the well will be reviewed, based
on a theoretical model of electron emission from the
grain. Furthermore, simulations will be presented regarding specific
laboratory, space and astrophysical applications.
[1] G.L. Delzanno, G. Lapenta, M. Rosenberg, Phys. Rev.
Lett., 92, 035002 (2004).
In this talk we discuss the analytic approximation to the loss
distribution of large conditionally independent heterogeneous portfolios. The
loss distribution is approximated by the expectation of some normal
distributions, which provides good overall approximation as well as tail
approximation. The computation is simple and fast as only numerical
integration is needed. The analytic approximation provides an excellent
alternative to some well-known approximation methods. We illustrate these
points with examples, including a bond portfolio with correlated default risk
and interest rate risk. We give an analytic expression for the expected
shortfall and show that VaR and CVaR can be easily computed by solving a
linear programming problem where VaR is the optimal solution and CVaR is the
optimal value.
An essential first step in many problems of numerical analysis and
computer graphics is to cover a region with a reasonably regular mesh.
We describe a short MATLAB code that begins with a "distance function"
to describe the region: $d(x)$ is the distance to the boundary
(with d
The talk will start with an introduction to recent development in Mathematica, with emphasis on numerical computing. This will be followed by a discussion of graph drawing algorithms for the display of relational information, in particular force directed algorithms. The talk will show that by employing multilevel approach and octree data structure, it is possible to achieve fast display of very large relational information, without compromising the quality.
Complete stochastic volatility models provide prices and
hedges. There are a number of complete models which jointly model an
underlying and one or more vanilla options written on it (for example
see Lyons, Schonbucher, Babbar and Davis). However, any consistent
model describing the volatility of options requires a complex
dependence of the volatility of the option on its strike. To date we
do not have a clear approach to selecting a model for the volatility
of these options