Mathematics

What I am Doing in Australia

Speaker: 
Jonathan Borwein
Date: 
Tue, May 17, 2011
Location: 
IRMACS Center, Simon Fraser University
Conference: 
JonFest 2011, Computation & Analytical Mathematics Conference
Abstract: 
Jonathan Borwein talks about his current research and the Priority Research Center for Computer Assisted Research Mathematics and its Applications (CARMA). Professor Borwein is both a Laureate Professor and the Director at CARMA which is located at the University of Newcastle in New South Wales, Australia.

Cloaking and Transformation Optics

Speaker: 
Gunther Uhlmann
Date: 
Mon, Jul 6, 2009
Location: 
University of New South Wales, Sydney, Australia
Conference: 
1st PRIMA Congress
Abstract: 
We describe recent theoretical and experimental progress on making objects invisible to detection by electromagnetic waves, acoustic waves and quantum waves. Maxwell's equations have transformation laws that allow for design of electromagnetic materials that steer light around a hidden region, returning it to its original path on the far side. Not only would observers be unaware of the contents of the hidden region, they would not even be aware that something was being hidden. The object, which would have no shadow, is said to be cloaked. We recount the recent history of the subject and discuss some of the mathematical and physical issues involved, especially the use of singular transformations.

Conformal Invariance and Universality in the 2D Ising Model

Speaker: 
Stalislav Smirnov
Date: 
Mon, Jul 6, 2009
Location: 
University of New South Wales, Sydney, Australia
Conference: 
1st PRIMA Congress
Abstract: 
It is conjectured that many 2D lattice models of physical phenomena (percolation, Ising model of a ferromagnet, self avoiding polymers, ...) become invariant under rotations and even conformal maps in the scaling limit (i.e. when "viewed from far away"). A well-known example is the Random Walk (invariant only under rotations preserving the lattice) which in the scaling limit converges to the conformally invariant Brownian Motion. Assuming the conformal invariance conjecture, physicists were able to make a number of striking but unrigorous predictions: e.g. dimension of a critical percolation cluster is almost surely 91/48; the number of simple length N trajectories of a Random Walk is about N11/32·mN, with m depending on a lattice, and so on. We will discuss the recent progress in mathematical understanding of this area, in particular for the Ising model. Much of the progress is based on combining ideas from probability, complex analysis, combinatorics.

Lagrangian Floer Homology and Mirror Symmetry

Speaker: 
Kenji Fukaya
Date: 
Tue, Jul 7, 2009
Location: 
University of New South Wales, Sydney, Australia
Conference: 
1st PRIMA Congress
Abstract: 
This is a survey of Lagrangian Floer homology which I developed together with Y.G.-Oh, Hiroshi Ohta, and Kaoru Ono. I will focus on its relation to (homological) mirror symmetry. The topic discussed include
  1. Definition of filtered A infinity algebra associated to a Lagrangian submanifold and its categorification.
  2. Its family version and how it is related to mirror symmetry.
  3. Some example including toric manifold. Calculation in that case and how mirror symmetry is observed from calculation.

Linearity in the Tropics

Speaker: 
Federico Ardila
Date: 
Tue, Jul 7, 2009
Location: 
University of New South Wales, Sydney, Australia
Conference: 
1st PRIMA Congress
Abstract: 
Tropical geometry studies an algebraic variety X by `tropicalizing' it into a polyhedral complex Trop(X) which retains much of the information about X. This technique has been applied successfully in numerous contexts in pure and applied mathematics.
Tropical varieties may be simpler than algebraic varieties, but they are by no means well understood. In fact, tropical linear spaces already feature a surprisingly rich and beautiful combinatorial structure, and interesting connections to geometry, topology, and phylogenetics. I will discuss what we currently know about them.

Categorical Crepant Resolutions of Higher Dimensional Simple Singularities

Speaker: 
Yujiro Kawamata
Date: 
Tue, Jul 7, 2009
Location: 
University of New South Wales, Sydney, Australia
Conference: 
1st PRIMA Congress
Abstract: 
Simple singularities in dimension 2 have crepant resolutions and satisfy the McKay correspondence. But higher dimensional generalizations do not. We propose the categorical crepant resolutions of such singularities in the sense that the Serre functors act as fractional shifts on the added objects.

Geometry and analysis of low dimensional manifolds

Speaker: 
Gang Tian
Date: 
Fri, Aug 7, 2009
Location: 
University of New South Wales, Sydney, Australia
Conference: 
1st PRIMA Congress
Abstract: 
In this talk, I will start with a brief tour on geometrization of 3-manifolds. Then I will discuss recent progresses on geometry and analysis of 4-manifolds.

On Fourth Order PDEs Modelling Electrostatic Micro-Electronical Systems

Speaker: 
Nassif Ghoussoub
Date: 
Wed, Jul 8, 2009
Location: 
University of New South Wales, Sydney, Australia
Conference: 
1st PRIMA Congress
Abstract: 

Micro-ElectroMechanical Systems (MEMS) and Nano-ElectroMechanical Systems (NEMS) are now a well established sector of contemporary technology. A key component of such systems is the simple idealized electrostatic device consisting of a thin and deformable plate that is held fixed along its boundary $ \partial \Omega $, where $ \Omega $ is a bounded domain in $ \mathbf{R}^2. $ The plate, which lies below another parallel rigid grounded plate (say at level $ z=1 $) has its upper surface coated with a negligibly thin metallic conducting film, in such a way that if a voltage l is applied to the conducting film, it deflects towards the top plate, and if the applied voltage is increased beyond a certain critical value $ l^* $, it then proceeds to touch the grounded plate. The steady-state is then lost, and we have a snap-through at a finite time creating the so-called pull-in instability. A proposed model for the deflection is given by the evolution equation

$$\frac{\partial u}{\partial t} - \Delta u + d\Delta^2 u = \frac{\lambda f(x)}{(1-u^2)}\qquad\mbox{for}\qquad x\in\Omega, t\gt 0 $$
$$u(x,t) = d\frac{\partial u}{\partial t}(x,t) = 0 \qquad\mbox{for}\qquad x\in\partial\Omega, t\gt 0$$
$$u(x,0) = 0\qquad\mbox{for}\qquod x\in\Omega$$

Now unlike the model involving only the second order Laplacian (i.e., $ d = 0 $), very little is known about this equation. We shall explain how, besides the above practical considerations, the model is an extremely rich source of interesting mathematical phenomena.

Law of Large Number and Central Limit Theorem under Uncertainty, the related New Itô's Calculus and Applications to Risk Measures

Speaker: 
Shige Peng
Date: 
Thu, Jul 9, 2009
Location: 
University of New South Wales, Sydney, Australia
Conference: 
1st PRIMA Congress
Abstract: 

Let $ S_n= \sum_{i=1}^n X_i $ where $ \{X_i\}_{i=1}^\infty $ is a sequence of independent and identically distributed (i.i.d.) of random variables with $ E[X_1]=m $. According to the classical law of large number (LLN), the sum $ S_n/n $ converges strongly to $ m $. Moreover, the well-known central limit theorem (CLT) tells us that, with $ m = 0 $ and $ s^2=E[X_1^2] $, for each bounded and continuous function $ j $ we have $ \lim_n E[j(S_n/\sqrt{n}))]=E[j(X)] $ with $ X \sim N(0, s^2) $.

These two fundamentally important results are widely used in probability, statistics, data analysis as well as in many practical situation such as financial pricing and risk controls. They provide a strong argument to explain why in practice normal distributions are so widely used. But a serious problem is that the i.i.d. condition is very difficult to be satisfied in practice for the most real-time processes for which the classical trials and samplings becomes impossible and the uncertainty of probabilities and/or distributions cannot be neglected.

In this talk we present a systematical generalization of the above LLN and CLT. Instead of fixing a probability measure P, we only assume that there exists a uncertain subset of probability measures $ \{P_q:q \in Q\} $. In this case a robust way to calculate the expectation of a financial loss $ X $ is its upper expectation: $ [\^\,(\mathbf{E})][X]=\sup_{q \in Q} E_q[X] $ where $ E_q $ is the expectation under the probability $ P_q $. The corresponding distribution uncertainty of $ X $ is given by $ F_q(x)=P_q(X \leq x) $, $ q \in Q $. Our main assumptions are:

  1. The distributions of $ X_i $ are within an abstract subset of distributions $ \{F_q(x):q \in Q\} $, called the distribution uncertainty of $ X_i $, with $ ['(m)]=[\^(\mathbf{E})][X_i]=\sup_q\int_{-\infty}^\infty xF_q(dx) $ and $ m=-[\^\,(\mathbf{E})][-X_i]=\inf_q \int_{-\infty}^\infty x F_q(dx) $.
  2. Any realization of $ X_1, \ldots, X_n $ does not change the distributional uncertainty of $ X_{n+1} $ (a new type of `independence' ).

Our new LLN is: for each linear growth continuous function $ j $ we have

$$\lim_{n\to\infty} \^{\mathbf{E}}[j(S_n/n)] = \sup_{m\leq v\leq ['(m)]} j(v)$$

Namely, the distribution uncertainty of $ S_n/n $ is, approximately, $ \{ d_v:m \leq v \leq ['(m)]\} $.

In particular, if $ m=['(m)]=0 $, then $ S_n/n $ converges strongly to 0. In this case, if we assume furthermore that $ ['(s)]2=[\^\,(\mathbf{E})][X_i^2] $ and $ s_2=-[\^\,(\mathbf{E})][-X_i^2] $, $ i=1, 2, \ldots $. Then we have the following generalization of the CLT:

$$\lim_{n\to\infty} [j(Sn/\sqrt{n})]= \^{\mathbf{E}}[j(X)], L(X)\in N(0,[s^2,\overline{s}^2]).$$

Here $ N(0, [s^2, ['(s)]^2]) $ stands for a distribution uncertainty subset and $ [\^(E)][j(X)] $ its the corresponding upper expectation. The number $ [\^(E)][j(X)] $ can be calculated by defining $ u(t, x):=[^(\mathbf{E})][j(x+\sqrt{tX})] $ which solves the following PDE $ \partial_t u= G(u_{xx}) $, with $ G(a):=[1/2](['(s)]^2a^+-s^2a^-). $

An interesting situation is when $ j $ is a convex function, $ [\^\,(\mathbf{E})][j(X)]=E[j(X_0)] $ with $ X_0 \sim N(0, ['(s)]^2) $. But if $ j $ is a concave function, then the above $ ['(s)]^2 $ has to be replaced by $ s^2 $. This coincidence can be used to explain a well-known puzzle: many practitioners, particularly in finance, use normal distributions with `dirty' data, and often with successes. In fact, this is also a high risky operation if the reasoning is not fully understood. If $ s=['(s)]=s $, then $ N(0, [s^2, ['(s)]^2])=N(0, s^2) $ which is a classical normal distribution. The method of the proof is very different from the classical one and a very deep regularity estimate of fully nonlinear PDE plays a crucial role.

A type of combination of LLN and CLT which converges in law to a more general $ N([m, ['(m)]], [s^2, ['(s)]^2]) $-distributions have been obtained. We also present our systematical research on the continuous-time counterpart of the above `G-normal distribution', called G-Brownian motion and the corresponding stochastic calculus of Itô's type as well as its applications.

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