Mathematical Sciences

School of Natural Sciences & Mathematics

Computational Science Seminar S14

Jan 31

Yannan Shen

Research Associate, Mathematical Sciences, UTD

Finite-Temperature Dynamics of Matter-Wave Dark Solitons in Linear and Periodic Potentials

We study matter-wave dark solitons in atomic Bose-Einstein condensates at finite temperatures, under the effect of linear and periodic potentials. Our model, namely a dissipative Gross-Pitaevskii equation, is treated analytically by means of dark soliton perturbation theory, which results in a Newtonian equation of motion for the dark soliton center. For sufficiently small wavenumbers of the periodic potential and weak linear potentials, the results are found to be in good agreement with pertinent ones obtained via a Bogoliubov-de Gennes analysis and direct numerical simulations.

Feb 21

David Lary

Associate Professor and Founding Director of the Center for Multi-scale Intelligent Integrated Interactive Sensing
Center for Space Science, Department of Physics, UTD

Multi-scale Multi-Platform Remote Sensing, Big Data and Machine Learning for Societal Benefit

We are at the dawn of a new era of discovery. Unprecedented amounts of data in multiple areas are becoming available and being archived. The data comes from a vast array of sources from Remote Sensing and Aerial vehicles, to wearable sensors, business analytics, news feeds, social media and everything in between. The ultimate goals of gathering these data sets are improved insights and objective data driven decisions. However, a very real obstacle to realizing this goal is the existence of data silos. Health care is a case in point. One set of silos is the massive amounts of Electronic Health Records (EHR) being collected in a variety of systems, not all of which are interoperable. Another set of silos contains the ever-increasing volumes of bioinformatics. Yet another set of silos is the environmental context. With environmental context coming from a wide variety of sources from socio-economic and demographic sources such as the census, to meteorological analyses, to remote sensing from multiple satellites, to environmental station networks, to aerial vehicles and the exciting new area of wearable sensors. A set of case studies with societal benefit will be presented.

Feb 28

Mac Hyman

Distinguished Professor of Mathematics,
Tulane University

This special event is sposnsored by the UTD/SMU SIAM Student Chapter

Good Choices for Great Careers

The choices that scientists make early in their careers will impact them for a lifetime. I will use the experiences of scientists who have had great careers to identify universal distinguishing traits of good career choices that can guild decisions in education, choice of profession, and job opportunities to increase your chances of having a great career with long-term sustained accomplishments. I ran a student internship program at Los Alamos National Laboratory for over 20 years. Recently, I have been tracking the careers past students and realized that the scientists with great careers weren’t necessarily the top students, and that some of the most brilliant students now had some of the most ho-hum careers. I will describe how the choices made by the scientists with great careers were based on following their passion, building their talents into a strength supporting their profession, and how they identified a supportive
engaging work environment. I will describe some simple guidelines that can help guide your choices, in school and in picking the right job that can lead to a rewarding career and more meaningful life. The topic is important because, so far as I can tell, life is not a trial run – we have one shot to get it right. The choices you are making right now to planning your career will impact your for a lifetime. Please join us for an engaging discussion on how to make the choices that will lead to a great career.

Mar 21

Weihua Geng

Department of Mathematics,
Southern Methodist University

A treecode-accelerated boundary integral Poisson-Botlzmann solver: modeling, algorithm and application

The Poisson-Boltzmann model is an extended model of the classical Gauss’s law involving additionally multiple dielectrics (thus interface problem), solvent effects (thus continuum model) and dissolved electrolytes (thus nonlinearity). In
this talk, I will introduce our recent progress in developing a numerical Poisson-Boltzmann solver with tree-code algorithms (for efficiency) and boundary integral formulation (for accuracy). Following that, I will briefly touch the attractive performance computing feature of this solver including parallelization and GPUs. I will conclude the talk with a report for the potential and established application of the Poisson-Botlzmann model/solver for computing quantities with biological significance such as electrostatic solvation energy, electrostatic forces, pKa values, etc.

Apr 4

Lev Gelb

Department of Materials Science and Engineering,
The University of Texas at Dallas

Analysis of Imaging Mass Spectrometry Data

Imaging Secondary Ion Mass Spectrometry (SIMS) experiments yield large data sets that are challenging to analyze. We will discuss the quantitative extraction of chemical concentration profiles, component spectra, and other information from such data. This will be done in the general framework of maximum a posteriori (MAP) reconstruction against physically motivated models, rather than through statistical dimensionality-reduction techniques such as Principal Component Analysis. Numerical techniques used to solve the resulting problem include alternating-least-squares iteration and simulated annealing. Selected topics will be presented, including the application of Gibbs priors for image regularization, extension of current analyses to include nonlinearities, and extraction of topographic information from high-mass-resolution images.

Apr 25

William Frensley

Department of Electrical Engineering,
The University of Texas at Dallas

The Many Levels of Semiconductor Device Physics

I will present a very quick tour of the logical chain that is presumed to exist between fundamental physics and active device technology, showing how the mathematical structures change with physical length scale, and illustrating the behavior at each level with simple numerical solutions and simulations. The macroscopic description consists of a set of partial differential equations. The point to be made is that the the conventional procedure (taking those equations to define a problem in modern analysis and constructing numerical solutions as dictated by asymptotic error analysis) produces a confidence in the results which is entirely unwarranted. Discrete numerical models, constructed by the simplest possible procedures directly from the physical system, come much closer to the true behavior of the device, because the discretization “errors” tend to correct the deficiencies in the macroscopic equations.