On the Qualitative Approach in Inverse Scattering for the Time Dependent Wave Equation

Speaker: 

Fioralba Cakoni

Institution: 

Rutgers University

Time: 

Monday, March 4, 2019 - 4:00pm to 5:00pm

Host: 

Location: 

RH 306

A recent trend in inverse scattering theory has focused on the development of a qualitative approach, which yield fast reconstructions with very little of a priori information  but at the expense of obtaining only limited information of the scatterer such as the  support, and estimates on the values of the constitutive parameters. Examples of such an approach are the linear sampling and factorization methods. These two methods are very well developed  in the time harmonic regime,  more generally for the underlying elliptic PDEs models, however for hyperbolic problems only limited results are available. In inverse scattering, the use  of  time domain  measurements  is  a remedy  for  large  amount  of  spatial  data  typically  needed  for the  application  of  qualitative  approach.

In this presentation we will discuss recent progress  in the development of linear sampling and factorization methods in the time domain.  Fist we consider the linear sampling method for solving inverse scattering problem for inhomogeneous media.  A   fundamental   tool   for  the justification of this method  is  the  solvability  of  the time  domain   interior   transmission   problem that relies on understanding the location on the complex plane of transmission eigenvalues. We present some latest results in this regard. The second problem addresses  the lack of mathematical rigorousness of the linear sampling method. In this context we discuss the factorization method to obtain explicit characterization of a (possibly non-convex) Dirichlet  scattering object from measurements of time-dependent causal scattered waves in the far field regime. In particular, we prove that far fields of  solutions to the wave equation due to particularly  modified  incident waves, characterize the obstacle  by a range criterion involving the square root of the time derivative  of the corresponding  far field operator. Our analysis  makes essential use of a coercivity property of the solution of the Dirichlet initial boundary value problem for the wave equation in the Laplace domain that forces us to consider this  particular modification of the far field operator. The latter in fact, can be chosen arbitrarily close to the true far field operator given in terms of physical measurements. Finally we discuss some related open questions. 

Concentration of Eigenfunctions: Sup-norms and Averages

Speaker: 

Jeffrey Galkowski

Institution: 

Stanford University

Time: 

Thursday, May 17, 2018 - 2:00pm

Host: 

Location: 

RH 340P

In this talk we relate concentration of Laplace eigenfunctions in position and momentum to sup-norms and submanifold averages. In particular, we present a unified picture for sup-norms and submanifold averages which characterizes the concentration of those eigenfunctions with maximal growth. We then exploit this characterization to derive geometric conditions under which maximal growth cannot occur. 

Arithmetic stability in p-adic towers of global function fields.

Speaker: 

Daqing Wan

Institution: 

UC Irvine

Time: 

Thursday, May 17, 2018 - 3:00pm to 4:00pm

Location: 

RH 306

Given a global function field K of characteristic p>0, the fundamental arithmetic invariants include the genus, the class number, the p-rank and more generally the slope sequence of the zeta function of K. In this expository lecture, we explore possible stability of these invariants in a p-adic Lie tower of K. Strong stability is expected when the tower comes from algebraic geometry, but this is already sufficiently interesting and difficult in the case of Zp towers.

From number theory to machine learning: hunting for smooth Boolean functions

Speaker: 

Roman Vershynin

Institution: 

UCI

Time: 

Tuesday, May 1, 2018 - 11:00am to 12:00pm

Location: 

RH 306

The most fundamental kind of functions studied in computer science are Boolean functions. They take n bits as an input and return one bit as an output. Most Boolean functions oscillate a lot, which is analogous to the fact that "most" continuous functions on R are nowhere differentiable. If we want to generate a "smooth" Boolean function, we can take the sign of some polynomial of low degree in n variables. Such functions are called polynomial threshold functions, and they are widely used in machine learning as classification devices. Surprisingly, we do not know how many polynomial threshold functions there are with a given degree! Even an approximate answer to this question has been known only for polynomials of degree 1, i.e. for linear functions. In a very recent joint work with Pierre Baldi, we found a way to approximately count polynomial threshold functions of any fixed degree. This solves a problem of M. Saks that goes back to 1993 and earlier. Our argument draws ideas from analytical number theory, additive combinatorics, enumerative combinatorics, probability and discrete geometry. I will describe some of these connections, focusing particularly on a beautiful interplay of zeta and Mobius funcitons in number theory, hyperplane arrangements in enumerative combinatorics and random tensors in probability theory.

Professor Svetlana Jitomirskaya Named Fellow by American Academy of Arts & Sciences

Congratulations to Professor Svetlana Jitomirskaya! She has been named a fellow by the American Academy of Arts & Sciences, one of the nation's oldest and most prestigious honorary societies. Professor Jitomirskaya works in the interplay between Mathematical Physics and Dynamical Systems. She is considered the leading expert on the spectral theory of Schrodinger operators with quasiperiodic potentials, and has published major results concerning the almost Mathieu operator, introduced by Peierls to describe Bloch electrons in a magnetic field.

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