## Upcoming Events

## 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## Jianfeng Lu, Duke University, Solving large-scale leading eigenvalue problem

The leading eigenvalue problems arise in many applications. When the dimension of the matrix is super huge, such as for applications in quantum many-body problems, conventional algorithms become impractical due to computational and memory complexity. In this talk, we will describe some recent works…

Find out more## November 2018

## Malgorzata Peszynska, Modeling hysteresis using ODEs with constraints: Numerical stability and other properties

In nonlinear conservation laws the flux function f(u) is usually single valued, but in many important applications it is hysteretic, i.e., it assigns different values depending on whether the input u(t) is increasing or decreasing in t. We present our recent results on…

Find out more## March 2019

## Casey Dietrich, NC State, Forecasting and Mapping of Coastal Flooding during Hurricanes

When a hurricane threatens North Carolina, researchers use computational models to predict how the ocean waters will rise, and what areas will be flooded. Emergency managers rely on fast and accurate storm surge predictions from these models to make decisions…

Find out more## Troy Butler, University of Colorado Denver, Data Consistent Inversion: An Interactive Talk Using Jupyter Notebooks

(Brief Note: In this talk, we utilize Jupyter notebooks to re-create some of our published results in real-time and also build a "computational intuition" for the ideas presented. In this way, we are (mostly) transparent about all the computations involved in our…

Find out more## April 2019

## Pierre Degond, Imperial College, London, Mathematical models of collective dynamics and self-organization

In this talk, I will review some mathematical challenges posed by the modeling of collective dynamics and self-organization. Then, I will focus on two specific problems, first, the derivation of fluid equations from particle dynamics of collective motion and second, the study of…

Find out more## August 2019

## Philippe Angot, Aix-Marseille Universite, Recent advances on vector penalty-projection methods for low-Mach multiphase flows with strong stresses and open boundary conditions

We discuss the efficiency of recent advances on the vector penalty-projection methods including the kinematic version which uses fast discrete Helmholtz-Hodge decompositions on edge-based generalized MAC-type unstructured meshes. These methods are especially designed for the computation of incompressible or low-Mach…

Find out more## September 2019

## Quoc Tran-Dinh, UNC-Chapel Hill Dept. of Statistics and Operations Research, Smooth Structures in Convex Functions and Applications to Proximal-Based Methods

In this talk, we demonstrate one way of exploiting smooth structures hidden in convex functions to develop optimization algorithms. Our key idea is to generalize a powerful concept so-called "self-concordance" introduced by Y. Nesterov and A. Nemirovskii to a broader class of convex functions.…

Find out more## Pedro Aceves Sanchez, NC State, Emergence of Vascular Networks

he emergence of vascular networks is a long-standing problem which has been the subject of intense research in the past decades. One of the main reasons being the widespread applications that it has in tissue regeneration, wound healing, cancer treatment, etc. The mechanisms involved in the formation of…

Find out more## October 2019

## Jon Stallrich, NC State, Sign-Informative Design and Analysis of Supersaturated Designs

Much of the literature on the design and analysis of supersaturated designs (SSDs), in which the number of factors exceeds the number of runs, rests on design principles assuming a least-squares analysis. More recently, researchers have discovered the potential of analyzing SSDs with penalized…

Find out more## Eric Hallman, NC State, Sharp 2-norm Error Bounds for LSQR and the Conjugate Gradient Method

When running any iterative algorithm it is useful to know when to stop. Here we review LSQR and LSLQ, two iterative methods for solving \min_x \|Ax-b\|_2 based on the Golub-Kahan bidiagonalization process, as well as estimates for the 2-norm error…

Find out more## Shahar Kovalsky, Duke University, Planar surface embeddings and non-convex harmonic maps

Mappings between domains are among the most basic and versatile tools used in the computational analysis and manipulation of shapes. Their applications range from animation in computer graphics to analysis of anatomical variation and anomaly detection in medicine and biology.…

Find out more## November 2019

## Misha Kilmer, Tufts University, A new tensor framework – theory and applications

Tensors (aka multiway arrays) can be instrumental in revealing latent correlations residing in high dimensional spaces. Despite their applicability to a broad range of applications in machine learning, speech recognition, and imaging, inconsistencies between tensor and matrix algebra have been…

Find out more## March 2020

## CANCELED: Bo Wang, Southern Methodist University, Fast and Accurate Simulations Of Time Domain Scattering Problem

This event has been rescheduled for August 25. We present a fast and accurate numerical method for the simulation of time domain scattering problem. Both acoustic and electromagnetic scattering problems are discussed. Nonreflecting boundary conditions (NRBCs) are used to truncate the problem.…

Find out more## April 2020

## Joseph Hart, Sandia National Laboratories, Hyper-Differential Sensitivity Analysis: Managing High Dimensional Uncertainty in Large-Scale Optimization

Large-scale optimization is ubiquitous in scientific and engineering applications. The end goal in most applications is the solution is a design, control, or inverse problem, constrained by complex high-fidelity models. Achieving this goal is challenging for many reasons, most notably,…

Find out more## September 2020

## Paris Perdikaris, University of of Pennsylvania, When and why physics-informed neural networks fail to train: A neural tangent kernel perspective

Physics-informed neural networks (PINNs) have lately received great attention thanks to their flexibility in tackling a wide range of forward and inverse problems involving partial differential equations. However, despite their noticeable empirical success, little is known about how such constrained…

Find out more## Craig Douglas, University of Wyoming, Applications of Data Assimilation Methods on a Coupled Dual Porosity Stokes Model

Porous media and conduit coupled systems are heavily used in a variety of areas such as groundwater system, petroleum extraction, and biochemical transport. A coupled dual porosity Stokes model has been proposed to simulate the fluid flow in a dual-porosity media and conduits…

Find out more## October 2020

## Abner J. Salgado, University of Tennessee, Knoxville, Fractional Gradient Flows

We consider a so-called fractional gradient flow: an evolution equation aimed at the minimization of a convex and l.s.c. energy, but where the evolution has memory effects. This memory is characterized by the fact that the negative of the (sub)gradient…

Find out more