# Graduate Analysis Seminar

Welcome to the home page of the Graduate Analysis Seminar. This page will list the past and upcoming talks for the seminar. For the Fall 2016 Semester, the seminar is being organized by Érik De Amorim, and will run at Mondays at 5pm in the Graduate Student Lounge (Hill 701).

In Spring 2016, the seminar was led by Matt Charnley. Please click here for the information from that semester.

Click here to return to my personal homepage, or here to return to the Rutgers Math Department Home Page.

### Next Seminar

### Talk Title: Linear Sampling Method

**Speaker: ** Matt Charnley

**Date: ** October 17, 2016

**Abstract: ** The inverse scattering problem, trying to locate an obstacle from either the far-field or near-field scattering data, is a highly non-linear problem. If the wavelength of the source is similar to the dimensions of the obstacle, the problem is inherently non-linear, and neither the high- or low-frequency approximations work. In this talk, I will discuss the so-called ``linear sampling method'' for solving this inverse problem. It reduces the problem to solving a linear integral equation at each point, and using the result to determine whether or not that point is contained in the obstacle. At the end, we will discuss the potential for applying this method in physical situations.

### Upcoming Seminars

### Past Seminars

### Talk Title: Hausdorff Dimemsion

**Speaker: ** Katie McKeon

**Date: ** October 10, 2016

**Abstract: ** I'll discuss a new technique (due to Professors Falk and Nussbaum) for efficiently estimating Hausdorff dimension of certain sets.

### Talk Title: Distributions, Fourier Transform, Lē-Sobolev Spaces and the Elliptic Regularity Theorem

**Speaker: ** Érik Amorim

**Date: ** October 3, 2016

**Abstract: ** I will talk about the things in the title, assuming no prior knowledge, and show how they can all be used together to prove the Elliptic Regularity Theorem. This theorem basically says that an elliptic PDE has the property that its solutions are as regular as you can expect, given the order of the equation, for an appropriate meaning of "regular".