17:00
Pressure and projection methods for viscous incompressible flows
Abstract
For incompressible Navier-Stokes equations in a bounded domain, I will
first present a formula for the pressure that involves the commutator
of the Laplacian and Leray-Helmholtz projection operators. This
commutator and hence the pressure is strictly dominated by the viscous
term at leading order. This leads to a well-posed and computationally
congenial unconstrained formulation for the Navier-Stokes equations.
Based on this pressure formulation, we will present a new
understanding and design principle for third-order stable projection
methods. Finally, we will discuss the delicate inf-sup stability issue
for these classes of methods. This is joint work with Bob Pego and Jie Liu.
15:45
A stochastic approach to relativistic diffusions
Abstract
A new class of relativistic diffusions encompassing all the previously studied examples has recently been introduced by C. Chevalier and F Debbasch, both in a heuristic and analytic way. Roughly speaking, they are characterised by the existence at each (proper) time (of the moving particle) of a (local) rest frame where the random part of the acceleration of the particle (computed using the time of the rest frame) is brownian in any spacelike direction of the frame.
I will explain how the tools of stochastic calculus enable us to give a concise and elegant description of these random paths on any Lorentzian manifiold. A mathematically clear definition of the the one-particle distribution function of the dynamics will emerge from this definition, and whose main property will be explained. This will enable me to obtain a general H-theorem and to shed some light on links between probablistic notions and the large scale structure of the manifold.
All necessary tools from stochastic calculus and geometry will be explained.
Twistor Methods for Scattering Amplitudes
Abstract
Tree-level scattering amplitudes in N=4 SYM are now known to possess a Yangian symmetry, formed by combining the original PSU(2,2|4) superconformal invariance with a second "dual" copy. I will also discuss very recent work constructing scattering amplitudes in a twistor space in which this dual superconformal symmetry acts geometrically.
Analysis of asymmetric stable droplets in a fish patterning model
Abstract
diffusion model which can be used to describe the patterning in a number of fish species. It is
straightforward to analyse this phenomenon in the case when two non-zero stable steady states are
symmetric, however the asymmetric case is more challenging. We use a recently developed
perturbation technique to investigate the weakly asymmetric case.
16:30
Eigenvalues of large random trees
Abstract
A common question in evolutionary biology is whether evolutionary processes leave some sort of signature in the shape of the phylogenetic tree of a collection of present day species.
Similarly, computer scientists wonder if the current structure of a network that has grown over time reveals something about the dynamics of that growth.
Motivated by such questions, it is natural to seek to construct``statistics'' that somehow summarise the shape of trees and more general graphs, and to determine the behaviour of these quantities when the graphs are generated by specific mechanisms.
The eigenvalues of the adjacency and Laplacian matrices of a graph are obvious candidates for such descriptors.
I will discuss how relatively simple techniques from linear algebra and probability may be used to understand the eigenvalues of a very broad class of large random trees. These methods differ from those that have been used thusfar to study other classes of large random matrices such as those appearing in compact Lie groups, operator algebras, physics, number theory, and communications engineering.
This is joint work with Shankar Bhamidi (U. of British Columbia) and Arnab Sen (U.C. Berkeley).
14:30
14:15
On the Modeling of Debt Maturity and Endogenous Default: A Caveat
Abstract
We focus on structural models in corporate finance with roll-over debt structure and endogenous default triggered by limited liability equity-holders. We point out imprecisions and misstatements in the literature and provide a rationale for the endogenous default policy.
Inverse problems in residual stress analysis and diffraction
Abstract
I wish to introduce several examples where the advancement of inverse problem methods can make a significant impact on applicatins.
1. Inverse eigenstrain analysis of residual stress states
2. Strain tomography
3. Strain image correlation
Depending on the time available, I may also mention (a) Rietveld refinement of diffraction patterns from polycrystalline aggregates, and
(b) Laue pattern indexing and energy dispersive detection for single grain strain analysis.
Diffusion in colloidal suspensions: Application to frost heave, tissue scaffolds and water purification
Vanishing cycles and Sebastiani-Thom in the setting of motivic integration II
Abstract
This is an overview, mostly of work of others (Denef, Loeser, Merle, Heinloth-Bittner,..). In the first part of the talk we give a brief introduction to motivic integration emphasizing its application to vanishing cycles. In the second part we discuss a join construction and formulate the relevant Sebastiani-Thom theorem.
Vanishing cycles and Sebastiani-Thom in the setting of motivic integration I
Abstract
This is an overview, mostly of work of others (Denef, Loeser, Merle, Heinloth-Bittner,..). In the first part of the talk we give a brief introduction to motivic integration emphasizing its application to vanishing cycles. In the second part we discuss a join construction and formulate the relevant Sebastiani-Thom theorem.
Generalized Nested Factorization - a recursive preconditioner for spatially discretized linear systems
11:00
Bayesian Gaussian Process models for multi-sensor time-series prediction
Abstract
processes (GPs). They are particularly useful for their flexibility,
facilitating accurate prediction even in the absence of strong physical models. GPs further allow us to work within a completely Bayesian framework. As such, we show how the hyperparameters of our system can be marginalised by use of Bayesian Monte Carlo, a principled method of approximate integration. We employ the error bars of the GP's prediction as a means to select only the most informative observations to store. This allows us to introduce an iterative formulation of the GP to give a dynamic, on-line algorithm. We also show how our error bars can be used to perform active data selection, allowing the GP to select where and when it should next take a measurement.
We demonstrate how our methods can be applied to multi-sensor prediction problems where data may be missing, delayed and/or correlated. In particular, we present a real network of weather sensors as a testbed for our algorithm.
Automorphic Forms, Galois Representations and Geometry
Representation growth of finitely generated nilpotent groups
Abstract
The study of representation growth of infinite groups asks how the
numbers of (suitable equivalence classes of) irreducible n-dimensional
representations of a given group behave as n tends to infinity. Recent
works in this young subject area have exhibited interesting arithmetic
and analytical properties of these sequences, often in the context of
semi-simple arithmetic groups.
In my talk I will present results on the representation growth of some
classes of finitely generated nilpotent groups. They draw on methods
from the theory of zeta functions of groups, the (Kirillov-Howe)
coadjoint orbit formalism for nilpotent groups, and the combinatorics
of (finite) Coxeter groups.
12:00
Hidden symmetries and decay for the wave equation outside a Kerr black hole
Abstract
This is joint work with Lars Andersson.