## Seminars and Colloquia by Series

Tuesday, November 1, 2016 - 14:05 , Location: Skiles 006 , Dr. Mehdi Vahab , Florida State University Math , Organizer: Martin Short
An adaptive hybrid level set moment-of-fluid method is developed to study
the material solidification of static and dynamic multiphase systems. The
main focus is on the solidification of water droplets, which may undergo
normal or supercooled freezing. We model the different regimes of freezing
such as supercooling, nucleation, recalescence, isothermal freezing and
solid cooling accordingly to capture physical dynamics during impact and
solidification of water droplets onto solid surfaces. The numerical
simulations are validated by comparison to analytical results and
experimental observations. The present simulations demonstrate the ability
of the method to capture sharp solidification front, handle contact line
dynamics, and the simultaneous impact, merging and freezing of a drop.
Parameter studies have been conducted, which show the influence of the
Stefan number on the regularity of the shape of frozen droplets. Also, it
is shown that impacting droplets with different sizes create ice shapes
which are uniform near the impact point and become dissimilar away from it.
In addition, surface wettability determines whether droplets freeze upon
impact or bounce away.
Monday, October 24, 2016 - 14:05 , Location: Skiles 005 , Prof. Lars Ruthotto , Emory University Math/CS , Organizer: Martin Short
Image registration is an essential task in almost all areas involving
imaging techniques. The goal of image registration is to find
geometrical correspondences between two or more images. Image
registration is commonly phrased as a variational problem that is known
to be ill-posed and thus regularization is commonly used to ensure
existence of solutions and/or introduce prior knowledge about the
application in mind. Many relevant applications, e.g., in biomedical
imaging, require that plausible transformations are diffeomorphic, i.e.,
smooth mappings with a smooth inverse.

This talk will present and compare two modeling strategies and numerical
approaches to diffeomorphic image registration. First, we will discuss
regularization approaches based on nonlinear elasticity. Second, we will
phrase image registration as an optimal control problem involving
hyperbolic PDEs which is similar to the popular framework of Large
Deformation Diffeomorphic Metric Mapping (LDDMM). Finally, we will
consider computational aspects and present numerical results for
real-life medical imaging problems.

Monday, October 17, 2016 - 14:05 , Location: Skiles 005 , Prof. Yanzhao Cao , Auburn University Mathematics , Organizer: Martin Short
A nonlinear filtering problem can be classified as a stochastic Bayesian
optimization problem of identifying the state of a stochastic dynamical
system based on noisy observations of the system. Well known numerical
simulation methods include unscented Kalman filters and particle
filters. In this talk, we consider a class of efficient numerical
methods based on forward backward stochastic differential equations.
The backward SDEs for nonlinear filtering problems are similar to the
Fokker-Planck equations for SDEs. We will describe the process of
deriving such backward SDEs as well as high order numerical algorithms
to solve them, which in turn solve nonlinear filtering problems.
Monday, October 3, 2016 - 14:00 , Location: Skiles 005 , , UW-Madison , , Organizer: Molei Tao
Many kinetic equations have the corresponding fluid limits. In the zero limit of the Knudsen number, one derives the Euler equation out of the Boltzmann equation and the heat equation out of the radiative transfer equation. While there are good numerical solvers for both kinetic and fluid equations, it is not quite well-understood when the two regimes co-exist. In this talk, we model the layer between the fluid and the kinetic using a half-space equation, study the well-posedness, design a numerical solver, and utilize it to couple the two sets of equations that govern separate domains. It is a joint work with Jianfeng Lu and Weiran Sun.
Monday, September 12, 2016 - 14:05 , Location: Skiles 005 , Prof. Jacob Eisenstein , GA Tech School of Interactive Computing , Organizer: Martin Short
Language change is a complex social phenomenon, revealing
pathways of communication and sociocultural influence. But while language
change has long been a topic of study in sociolinguistics, traditional
linguistic research methods rely on circumstantial evidence, estimating the
direction of change from differences between older and younger speakers. In
this research, we use a data set of several million Twitter users to track
language changes in progress. First, we show that language change can be
viewed as a form of social influence: we observe complex contagion for
netspeak'' abbreviations (e.g., lol) and phonetic spellings, but not for
older dialect markers from spoken language. Next, we test whether specific
types of social network connections are more influential than others, using
a parametric Hawkes process model. We find that tie strength plays an
important role: densely embedded social ties are significantly better
conduits of linguistic influence. Geographic locality appears to play a
more limited role: we find relatively little evidence to support the
hypothesis that individuals are more influenced by geographically local
social ties, even in the usage of geographical dialect markers.
Monday, August 8, 2016 - 14:00 , Location: Skiles 006 , , UNIST, Korea , Organizer: Sung Ha Kang
Inspired by the usefulness of difference of convex functions in some problems, e.g. sparse representations, we use such an idea of difference of convex functions to propose a method of finding an eigenfunction of a self-adjointoperator. In a matrix setting, this method always finds an eigenvector of a symmetric matrix corresponding to the smallest eigenvalue without solving Ax=b. In fact, such a matrix A is allowed to be singular, as well. We can apply the same setting to a generalized eigenvalue problem. We will discuss its convergence as well.
Wednesday, June 22, 2016 - 11:00 , Location: Skiles 006 , , Istituto Italiano di Tecnologia (Italy) , Organizer: Sung Ha Kang
Symmetric positive definite (SPD) matrices play important roles in numerous areas of mathematics, statistics, and their applications in machine learning, optimization, computer vision, and related fields. Among the most important topics in the study of SPD matrices are the distances between them that can properly capture the geometry of the set of SPD matrices. Two of the most widely used distances are the affine-invariant distance and the Log-Euclidean distance, which are geodesic distances corresponding to two different Riemannian metrics on this set. In this talk, we present our recently developed concept of Log-Hilbert-Schmidt (Log-HS) distance between positive definite Hilbert-Schmidt operators on a Hilbert space.This is the generalization of the Log-Euclidean distance between SPD matrices to the infinite-dimensional setting. In the case of reproducing kernel Hilbert space (RKHS) covariance operators, we obtain closed form formulas for the Log-HS distance, expressed via Gram matrices. As a practical example, we demonstrate an application of the Log-HS distance to the problem of image classification in computer vision.
Monday, April 11, 2016 - 14:05 , Location: Skiles 005 , Byungmoon Kim , Adobe Research , Organizer: Yingjie Liu
This talk will tell the story on using simulation for painting. I will tell a few of
projects that had simulation and painting involved. One is iPad-based ultra-low-cost real
time simulation of old photography process to compute effects that modern day users may
find interesting. The other is more full-blown fluid simulation for painting using
highest-end GPU. Even with massive processing power of GPU, real time high fidelity
painting simulation is hard since computation budget is limited. Basically we should deal
with large errors. It may sound odd if someone says that very low-accuracy simulation is
interesting - but this is very true. In particular, we tried to pull most pressure effect
out from about 10 Jacobi iterations that we could afford. I would like to share my
experience on improving fixed number of fixed point iterations.
Friday, April 8, 2016 - 14:00 , Location: Skiles 005 , , Department of Mathematics, University of Georgia , Organizer: Sung Ha Kang
Bivariate splines are smooth piecewise polynomial functions defined on a triangulation of arbitrary polygon. They are extremely useful for numerical solution of PDE, scattered data interpolation and fitting, statistical data analysis, and etc.. In this talk, I shall explain its new application to a biological study. Mainly, I will explain how to use them to numerically solve a type of nonlinear diffusive time dependent PDE which arise from a biological study on the density of species over a region of interest. I apply our spline solution to simulate a real life study on malaria diseases in Bandiagara, Mali. Our numerical result show some similarity with the pattern from the biological study in2013 in a blind testing. In addition, I shall explain how to use bivariate splines to numerically solve several systems of diffusive PDEs: e.g. predator-prey type, resource competing type and other type systems.
Monday, April 4, 2016 - 14:00 , Location: Skiles 005 , Wuchen Li , Georgia Tech Mathematics , Organizer: Martin Short
Fokker-Planck equations, along with stochastic differential equations, play vital roles in physics, population modeling, game theory and optimization (finite or infinite dimensional). In this thesis, we study three topics, both theoretically and computationally, centered around them.In part one, we consider the optimal transport for finite discrete states, which are on a finite but arbitrary graph. By defining a discrete 2-Wasserstein metric, we derive Fokker-Planck equations on finite graphs as gradient flows of free energies. By using dynamical viewpoint, we obtain an exponential convergence result to equilibrium. This derivation provides tools for many applications, including numerics for nonlinear partial differential equations and evolutionary game theory.In part two, we introduce a new stochastic differential equation based framework for optimal control with constraints. The framework can efficiently solve several real world problems in differential games and Robotics, including the path-planning problem.In part three, we introduce a new noise model for stochastic oscillators. With this model, we prove global boundedness of trajectories. In addition, we derive a pair of associated Fokker-Planck equations.