Center for Applied Mathematical Sciences
Colloquia for the Spring 2013 Semester

Vlad Vicol
Princeton University
Monday, January 28 KAP 414 3:30 PM - 4:30 PM
On the inviscid limit for the stochastic Navier-Stokes equations

We discuss recent results on the behavior in the infinite Reynolds number limit of invariant measures for the 2D stochastic Navier-Stokes equations. We prove that the limiting measures are supported on bounded vorticity solutions of the 2D Euler equations. Invariant measures provide a canonical object which can be used to link the fluids equations to the heuristic statistical theories of turbulent flow.
Motivated by 2D turbulence considerations we are lead to consider the problem of well-posedness for the stochastic 2D Euler equations. This is joint work with Nathan Glatt-Holtz and Vladimir Sverak.




Monday, February 4 KAP 414 3:30 PM - 4:30 PM
The Whiteman Lecture


Daniel Tataru
UC Berkeley
Monday, February 11 KAP 414 3:30 PM - 4:30 PM
Geometric PDE's

The aim of this talk is to present a group of geometric pde's where the objects of study are maps into manifolds. Examples include the harmonic maps, the harmonic heat flow, wave maps and Schroedinger maps.
Some recent results and ideas will be surveyed.


Guenther Walther
Stanford University
Monday, February 25 KAP 414 3:30 PM - 4:30 PM
Optimal and fast detection with the scan and with the average likelihood ratio

Scan statistics are the standard tool for a range of detection problems, such as the detection of spatial disease clusters. There have been a number of recent claims in the literature, based on empirical findings,that scan statistics are inferior to an approach that involves the average likelihood ratio. The talk will look into this issue and present heuristics as well as mathematical optimality results. If time permits, a connection to efficient algorithms will be discussed.


Ronald Graham

Monday, March 4 KAP 414 2:00 PM - 3:00 PM
Special Time
Juggling Mathematics and Magic

The mystery of magic and the art of juggling have surprising links to interesting ideas from mathematics. In this talk, I will illustrate some of these connections.


Fan Chung Graham
UC San Diego
Monday, March 4 KAP 414 3:30 PM - 4:30 PM
Can you hear the shape of a network? New directions in spectral graph theory

We will discuss some recent developments in several new directions of spectral graph theory, including random walks for directed graphs, ranking algorithms, graph gauge theory, network games, graph limits and graphlets, for example.


Steve Shkoller
UC Davis
Wednesday, March 13 KAP 414 3:30 PM - 4:30 PM
Free-boundary problems in fluid dynamics

Free-boundary problems in fluid dynamics involve solutions to Euler equations on domains which are evolving in time and which are a priori unknown. The boundaries of these fluid domains have evolution laws which require knowledge of the fluid flow, while solving for the fluid flow, in turn, requires information about the geometry of the boundary. Such moving boundaries arise in the study of ocean waves, shock waves, phase transitions, and many other interesting wave patterns. In this lecture, I will survey some of the recent results in this area.


Maria Schonbek
UC Santa Cruz
Monday, April 8 KAP 414 3:30 PM - 4:30 PM
L^2 asymptotic stability of mild Navier-Stokes solutions.

We consider the initial value problem for the Navier-Stokes equations modeling an incompressible fluid in three dimensions. It is well-known that this problem has a unique global-in-time mild solution for a suciently small initial condition u0 and for a small external force F in suitable scaling invariant spaces. We show that these global-in-time mild solutions are asymptotically stable under every (arbitrary large) L2-perturbation of their initial conditions.
The work is joint with Grsegorz Karch and Dominika Pilarczyk.


John A. Burns
Virginia Tech.
Monday, April 15 KAP 414 3:30 PM - 4:30 PM
Parabolic Boundary Control Problems with Delayed Actuator Dynamics

In this talk we discuss some control, optimization and design problems for a convection diffusion equation and investigate the impact of including actuator dynamics and delays. The problem is motivated by applications to control and design of energy efficient buildings where actuation is provided by a HVAC system. To provide some indication of the scope of this problem, it is helpful to note that buildings worldwide account for a approximately 40% of global energy consumption, and the resulting greenhouse gas emissions, significantly exceed those of all transportation combined. In the United States a 50% reduction in buildings energy usage is equivalent to taking every passenger vehicle and small truck in the United States off the road and a 70% reduction in buildings energy usage is equivalent to eliminating the entire energy consumption of the U.S. transportation sector.
As a mathematical object, a whole building system is the composition of diverse dynamic subsystems and is a complex, multi-scale, nonlinear, and uncertain dynamical system. Recent results have shown that by considering the whole building as an integrated system and applying modern estimation and control techniques to optimize this system, one can achieve greater efficiencies than obtained by optimizing individual building components such as lighting and HVAC. In order to control a whole building system for energy minimization one must address a variety of theoretical and computational science problems at various levels from room to complete building envelopes. We focus on a single room example where the basic model is governed by a parabolic partial differential equation which is augmented to include a model of an actuator with delays. We show that under suitable conditions, the coupled system is well posed in a standard Hilbert space and we use this corresponding abstract formulation to construct numerical methods for control design.


Stephen Childress
Courant Institute
Monday, April 22 KAP 414 3:30 PM - 4:30 PM
In search of a stable jelly-fish like flying machine.

Flapping-wing aircraft offer an alternative to helicopters in achieving maneuverable flight at small scales, although stabilizing such aircraft remains a key challenge. Experimental studies of the stable hovering of rigid structures in an oscillating airflow suggest a design which mimics the flapping contractions of a jelly fish. We have constructed a prototype of such a craft, and test flown it in free flight with an external power source. Our results indicate the possibility of passively stable hovering of a flapping-wing craft.


Charles Doering
U of Michigan
Monday, April 29 KAP 414 3:30 PM - 4:30 PM
``Ultimate state'' of two-dimensional Rayleigh-Bénard convection

Rayleigh-Bénard convection is the buoyancy-driven flow of a fluid heated from below and cooled from above. Heat transport by convection an important physical process for applications in engineering, atmosphere and ocean science, and astrophysics, and it serves as a fundamental paradigm of modern nonlinear dynamics, pattern formation, chaos, and turbulence.
Determining the bulk transport properties of high Rayleigh number convection turbulent convection remains a grand challenge for experiment, simulation, theory, and analysis. In this talk, after a general survey of the theory and applications of Rayleigh-Bénard convection we describe recent results for mathematically rigorous upper limits on the vertical heat transport in two dimensional Rayleigh-Bénard convection between stress-free isothermal boundaries derived from the Boussinesq approximation of the Navier-Stokes equations. These bounds challenge some popular theoretical arguments regarding the asymptotic high Rayleigh number heat transport scaling.

Colloquia for the Fall 2012 Semester

David Kempe
Computer Science, USC
Monday, September 17 KAP 414 3:30 PM - 4:30 PM
Learning Social Similarities from Friendship Patterns

What can a social network tell us about the underlying latent "social structure", the way in which individuals are similar or dissimilar?
Much of social network analysis is, implicitly or explicitly, predicated on the assumption that individuals tend to be more similar to their friends than to strangers. Having explicit access to similarity information instead of merely the noisy signal provided by the presence or absence of edges could improve analysis significantly.
We study the following natural question: Given a social network - reflecting the underlying social distances between its nodes - how accurately can we reconstruct the social structure?

It is tempting to model similarities and dissimilarities as distances, so that the social structure is a metric space. However, observed social networks are usually multiplex, in the sense that different edges originate from similarity in one or more among a number of different categories, such as geographical proximity, professional interactions, kinship, or hobbies.
Since close proximity in even one category makes the presence of edges much more likely, an observed social network is more accurately modeled as a union of separate networks. In general, it is a priori not known which network a given edge comes from.
While generative models based on a single metric space have been analyzed previously, a union of several networks individually generated from metrics is structurally very different from (and more complex than) networks generated from just one metric.

We begin to address this reconstruction problem formally. The latent "social structure" consists of several metric spaces. Each metric space gives rise to a "distance-based random graph," in which edges are created according to a distribution that depends on the underlying metric space and makes long-range edges less likely than short ones. For a concrete model, we consider Kleinberg's small-world model and some variations thereof. The observed social network is the union of these graphs. All edges are unlabeled, in the sense that the existence of an edge does not reveal which random graph it comes from.
Our main result is a near-linear time algorithm which reconstructs from this unlabeled union each of the individual metrics with provably low distortion.

(Joint work with Ittai Abraham Shiri Chechik, and Aleksandrs Slivkins.)


Ilia Zaliapin
University of Nevada-Reno
Monday, September 24 KAP 414 3:30 PM - 4:30 PM
Self-similarity of random trees and applications

Hierarchical branching organization is ubiquitous in nature. It is readily seen in river basins, drainage networks, bronchial passages, botanical trees, lightening, and snowflakes, to mention but a few. Empirical evidence reveals a surprising similarity among various natural hierarchies – many of them are closely approximated by so-called self-similar trees (SSTs). The Horton and Tokunaga branching laws provide a flexible framework for studying self-similarity in random trees. The Horton self-similarity is a weaker property that addresses the principal branching in a tree; it is a counterpart of the power-law size distribution for elements of a branching system. The stronger Tokunaga self-similarity addresses so-called side branching; it ensures that different levels of a hierarchy have the same probabilistic structure (in a sense to be defined). The Horton and Tokunaga self-similarity have been empirically established in numerous observed and modeled systems, including river networks, vein structure of botanical leaves, diffusion limited aggregation, two dimensional site percolation, and nearest-neighbor clustering in Euclidean spaces. The diversity of these processes and models hints at the existence of a universal underlying mechanism responsible for the Tokunaga self-similarity and prompts the question: What basic probability models can generate Tokunaga self-similar trees with a range of parameters? We review the existing results on self-similarity for the critical binary Galton-Watson tree and present recent findings on self-similarity for tree representation of coalescent processes, random walks, and white noises. In particular, we establish the equivalence of tree representation for selected coalescent processes and time series models. The presented results suggest at least a partial explanation for the omnipresence of Tokunaga self-similar structures in natural branching systems. The results are illustrated using applications in hydrology, seismology, and billiard dynamics.


John Stillwell
University of San Francisco (joint with the department colloquium)
Wednesday, October 3 KAP 414 3:30 PM - 4:30 PM
Poincaré and the early history of 3-manifolds

The name of Poincar remains well known in low-dimensional topology, 100 years after his death, thanks to the recent solution of his famous conjecture about the 3-sphere. In this talk we will try to recreate the world of 3-manifolds in the time of Poincar´e and his immediate successors, with special attention to the contributions of Heegaard, Wirtinger, Tietze, Dehn and Alexander.


Bradley Efron
Stanford University
Monday, October 15 GFS 106 3:45 PM - 4:45 PM
Special Time & Location
Bayes and Empirical Bayes Information (Learning from the experience of others)

Bayesian methods require a catalog of prior experience for the interpretation of statistical evidence. In the absence of prior information, empirical Bayes methods rely instead on a catalog of cases similar to the problem of interest. The crime rate in one small city, for example, may be estimated by modifying its observed rate with evidence from other cities.
I will give some examples that show how powerful the empirical Bayes approach can be in practice, both for estimation and testing. The use of "other" cases then raises the question of just which others are relevant, and how their information bears on the case of interest.


Venugopal Veeravalli
University of Illinois-Urbana
Monday, October 22 KAP 414 3:30 PM - 4:30 PM
Data-Efficient Quickest Change Detection

In the classical quickest change detection problem, there is a sequence of observations whose distribution changes at an unknown time, and the goal is to detect the change as quickly as possible, subject to a false alarm constraint. In many engineering applications of quickest change detection, there may be a cost associated with acquiring observations. We therefore consider the quickest change detection problem with an additional constraint on the cost of the observations used in the detection process, i.e., we seek algorithms that are data-efficient or energy-efficient. The objective is to select an observation control policy along with the stopping time at which the change is declared, so as to minimize the average detection delay, subject to both a false alarm constraint as well as a constraint on the average number of observations used before the change point. We consider both Bayesian and minimax formulations of the problem, and extensions to the setting where the observations are available at a set of distributed sensors. In all of these cases, we develop algorithms that are first-order asymptotically optimal as the false alarm rate goes to zero. We also demonstrate through numerical studies that our algorithms can be considerably more efficient than algorithms based on fractional sampling, where the observations to be skipped are determined a priori in order to meet the observation constraint.

(This is joint work with Taposh Banerjee.)


Juhi Jang
University of California-Riverside
Monday, October 29 KAP 414 3:30 PM - 4:30 PM
Stability theory of polytropic gaseous stars

I'll discuss stability theory of Lane-Emden equilibrium stars under Euler-Poisson or Navier-Stokes-Poisson system. A linear stability can be characterized by the adiabatic exponent. A nonlinear instability will be also discussed.


Christine Shoemaker
Cornell University
Monday, November 5 KAP 414 3:30 PM - 4:30 PM
Surrogate Surface Algorithms for Nonlinear and Global Optimization and Uncertainty Analysis of Computationally Expensive Simulation Models

Optimization and uncertainty analyses used in conjunction with complex simulation models are important for using models to make predictions based on observations and for finding optimal designs or policies. Often these models can generate objective function surfaces with multiple local minima. Global Optimization and uncertainty analysis typically require a very large number of simulations, often thousands or tens of thousands. However, this number of simulations is not feasible for computationally expensive nonlinear simulation models.
Our approach is to iteratively approximate the objective function or likelihood function f(x) with Radial Basis Functions (RBF) or other surrogate response surfaces during the search process. Our methods are derivative-free and can find local and global minima. It is this use of previously evaluated points f(xi) that is responsible for great savings in computational time. I will give results that compare these algorithms , including applications to complex simulations for groundwater remediation and carbon sequestration and for uncertainty quantification.


Roman Shvydkoy
University of Illinois-Chicago
Monday, November 19 KAP 414 3:30 PM - 4:30 PM
Energetics of the Euler equation and self-similar blow-up

The existence of self-similar blow-up for the viscous incompressible fluids was a classical question settled in the seminal of works of Necas, et al and Tsai in the 90'. The corresponding scenario for the inviscid Euler equations has not received as much attention, yet it appears in many numerical simulations, most prominently in those based on vortex filament models of Kida's high symmetry flows. The case of a homogeneous self-similar profile is especially interesting due to its relevance to other theoretical questions such the Onsager conjecture or existence of Landau type solutions. In this talk we give an account of recent studies demonstrating that a self-similar blow-up is unsustainable the Euler system under various weak assumptions on the profile. We will talk about general energetics of the Euler system that, in part, is responsible for such exclusion results.