## Upcoming Events

## March 2017

## Dirk Lorenz, TU Braunschweig, “Randomized sparse Kaczmarz methods”

The Kaczmarz method is a numerical method to solve systems of linear equations and compute minimum-norm solutions of underdetermined systems. Because the method has very low memory requirements it has gained new attention in recent years. In this talk we…

Find out more## Junping Wang, NSF, “Primal-dual weak Galerkin finite element methods for PDEs”

This talk will introduce a primal-dual finite element method for variational problems where the trial and test spaces are different. The essential idea behind the primal-dual method is to formulate the original problem as a constrained minimization problem. The corresponding…

Find out more## August 2017

## Jichun Li, University of Nevada Las Vegas (UNLV), Electromagnetic cloaking: mathematical analysis and simulation

In June 23, 2006's "Science" magazine, Pendry et al and Leonhardt independently published their papers on electromagnetic cloaking. In Nov.10, 2006's Science magazine, Pendry et al demonstrated the first practical realization of such a cloak with the use of artificially constructed…

Find out more## September 2017

## Eric Chi, NC State Statistics, Convex Co-clustering of Tensors

Clustering is a fundamental unsupervised learning technique that aims to discover groups of objects in a dataset. Biclustering extends clustering to two dimensions where both observations and variables are grouped simultaneously, such as clustering both cancerous tumors and genes or…

Find out more## Shu-Cherng Fang, NC State ISE, Linear Reformulation of Polynomial Discrete Programming for Fast Computation

Optimization models involving a polynomial objective function and multiple polynomial constraints with discrete variables are often encountered in engineering, management and systems. Treating the non-convex cross-product terms is the key. State-of- the-art methods usually convert such a problem into a…

Find out more## Sung Ha Kang, Georgia Tech, Variational image processing and computational challenges

Starting with an introduction to variational/PDE based image processing, this talk will focus on new developments of fast algorithms for higher order variational imaging models. For example, recent developments of fast algorithms, based on operator splitting, augmented Lagrangian, and alternating…

Find out more## October 2017

## Murthy Gudatti, NC State, Efficient Forward and Inverse Algorithms for Guided Wave Inversion

Guided waves are widely utilized in the fields of nondestructive testing and geophysical inversion, to estimate the medium properties through inversion of the dispersion curves. In this talk, we present improved methodologies for computing both dispersion curves and their derivatives, the two main ingredients…

Find out more## Sercan Yildiz, SAMSI,Polynomial Optimization with Sums-of-Squares Interpolants

Sums-of-squares certificates define a hierarchy of relaxations for polynomial optimization problems which are parametrized with the degree of the polynomials in the sums-of-squares representation. Each level of the hierarchy generates a lower bound on the true optimal value, which can…

Find out more## November 2017

## Serkan Gugercin, Virginia Tech, Interpolatory model reduction with applications to flow control and nonlinear inversion

Numerical simulation of large-scale dynamical systems plays a crucial role and may be the only possibility in studying a great variety of complex physical phenomena with applications ranging from heat transfer to fluid dynamics, to signal propagation and interference in…

Find out more## February 2018

## Georg Stadler, Sparsity meets optimal control of PDEs

I will discuss formulations and algorithms for computing sparse optimal controls in systems governed by PDEs. These sparse solutions can guide the placement of control devices in applications. After reviewing results for elliptic and parabolic PDEs, I will focus on recent work on sparse…

Find out more## Grey Ballard, Wake Forest University, Tensor Decompositions for Multidimensional Data Analysis

An increasing number of scientific and enterprise data sets are multidimensional, where data is gathered for every configuration of three or more parameters. For example, physical simulations often track a set of variables in two or three spatial dimensions over…

Find out more## Wilkins Aquino, Duke University, A Locally Adapted Reduced Basis Method for Solving Risk-Averse PDE-Constrained Optimization Problems

The numerical solution of large-scale risk-averse PDE-constrained optimization problems requires substantial computational effort due to the discretization in physical and stochastic dimensions. Managing the cost is essential to tackle such problems with high dimensional uncertainties. In this work, we combine an inexact trust-region…

Find out more## March 2018

## Gabor Pataki, UNC-Chapel Hill, Bad semidefinite programs, linear algebra, and short proofs

Semidefinite programs (SDPs) -- optimization problems with linear constraints, linear objective, and semidefinite matrix variables -- are some of the most useful, versatile, and pervasive optimization problems to emerge in the last 30 years. They find applications in combinatorial optimization, machine learning, and statistics, to…

Find out more## Yingwei Wang, University of Wisconsin, Madison, Introduction to Muntz Polynomial Approximation

In general, solutions to the Laplacian equation enjoy relatively high smoothness. However, they can exhibit singular behaviors at domain corners or points where boundary conditions change type. In this talk, I will focus on the mixed Dirichlet-Neumann boundary conditions for…

Find out more## April 2018

## Mingchao Cai, Morgan State University, Some Fast Solvers for Poroelastic Models

Poroelastic models have been widely used in Biomechanics. For example, modeling brain edema and cancellous bones. We aim at solving the Biot model under the MAC Finite Difference discretization and the stabilized finite element discretizations. To solve the resulting saddle point linear…

Find out more## Daniel B. Szyld, Temple University, Asynchronous Optimized Schwarz Methods for the solution of PDEs

Asynchronous methods refer to parallel iterative procedures where each process performs its task without waiting for other processes to be completed, i.e., with whatever information it has locally available and with no synchronizations with other processes. In this talk, an asynchronous version of the…

Find out more## Mario Ricchiuto, INRIA Bordeaux, On dispersive-like effects in channels with banks

The study of the propagation of undular bores in channels is relevant to many applications which go from the propagation of tsunami waves, to that of tidal bores/waves, to the propagation of strong waves in manmade channels due to hazards (e.g. dam breaking).…

Find out more## September 2018

## Pierre Gremaud, NC State, Advances and challenges in global sensitivity analysis

What to do when the size and complexity of your model essentially prevent you from using it? Well, get a smaller and simpler model... At the heart of this dimension reduction process is the notion of parameter importance which, ultimately,…

Find out more## Susan Minkoff, UT Dallas, Microseismic Source Estimation via Seismic Inversion

Accurate estimation of microseismic events (small earthquakes) generated during hydraulic fracturing of low permeability rocks such as shale enables important characterization of hydraulic fracture networks. Determining the orientation of the fracture is important in characterizing the effectiveness of the stimulation…

Find out more## October 2018

## Vladimir Druskin, Worcester Polytechnic Institute, Reduced Order Models, Networks and Applications to Modeling and Imaging with Waves

Geophysical seismic exploration, as well as radar and sonar imaging, require the solution of large scale forward and inverse problems for hyperbolic systems of equations. In this talk, I will show how model order reduction can be used to address…

Find out more