- You are here:
- GT Home
- Home
- News & Events

Monday, March 26, 2012 - 14:00 ,
Location: Skiles 006 ,
Edmond Chow ,
School of Computational Science and Engineering, Georgia Institute of Technology ,
Organizer: Sung Ha Kang

Brownian dynamics (BD) is a computational technique for simulating the motions of molecules interacting through hydrodynamic and non-hydrodynamic forces. BD simulations are the main tool used in computational biology for studying diffusion-controlled cellular processes. This talk presents several new numerical linear algebra techniques to accelerate large BD simulations, and related Stokesian dynamics (SD) simulations. These techniques include: 1) a preconditioned Lanczos process for computing Brownian vectors from a distribution with given covariance, 2) low-rank approximations to the hydrodynamic tensor suitable for large-scale problems, and 3) a reformulation of the computations to approximate solutions to multiple time steps simultaneously, allowing the efficient use of data parallel hardware on modern computer architectures.

Monday, March 5, 2012 - 14:00 ,
Location: Skiles 006 ,
Prof. Di Liu ,
Depatment of Mathematics, Michigan State Univeristy ,
Organizer: Haomin Zhou

Multiscale and stochastic approaches play a crucial role in faithfully capturing the dynamical features and making insightful predictions of cellular reacting systems involving gene expression. Despite theiraccuracy, the standard stochastic simulation algorithms are necessarily inefficient for most of the realistic problems with a multiscale nature characterized by multiple time scales induced by widely disparate reactions rates. In this talk, I will discuss some recent progress on using asymptotic techniques for probability theory to simplify the complex networks and help to design efficient numerical schemes.

Monday, February 27, 2012 - 14:05 ,
Location: Skiles 006 ,
Marcus Roper ,
UCLA Mathematics Dept. ,
Organizer:

Although fungi are the most diverse eukaryotic organisms, we

have only a very fragmentary understanding of their success in so many

niches or of the processes by which new species emerge and disperse. I

will discuss how we are using math modeling and perspectives from

physics and fluid mechanics to understand fungal life histories and

evolution:

have only a very fragmentary understanding of their success in so many

niches or of the processes by which new species emerge and disperse. I

will discuss how we are using math modeling and perspectives from

physics and fluid mechanics to understand fungal life histories and

evolution:

#1. A growing filamentous fungi may harbor a diverse population of

nuclei. Increasing evidence shows that this internal genetic

flexibility is a motor for diversification and virulence, and helps

the fungus to utilize nutritionally complex substrates like plant cell

walls. I'll show that hydrodynamic mixing of nuclei enables fungi to

manage their internal genetic richness.

#2. The forcibly launched spores of ascomycete fungi must eject

through a boundary layer of nearly still air in order to reach

dispersive air ﬂows. Individually ejected microscopic spores are

almost immediately brought to rest by fluid drag. However, by

coordinating the ejection of thousands or hundreds of thousands of

spores fungi, such as the devastating plant pathogen Sclerotinia

sclerotiorum are able to create a flow of air that carries spores

across the boundary layer and around any intervening obstacles.

Moreover the physical organization of the jet compels the diverse

genotypes that may be present within the fungus to cooperate to

disperse all spores maximally.

Monday, February 20, 2012 - 14:00 ,
Location: Skiles 006 ,
Benjamin Berkels ,
South Carolina University ,
Organizer: Sung Ha Kang

Image registration is the task of transforming different images, or more general data sets, into a common coordinate system. In this talk, we employ a widely used general variational formulation for the registration of image pairs. We then discuss a general gradient flow based minimization framework suitable to numerically solve the arising minimization problems. The registration framework is next extended to handle the registration of hundreds of consecutive images to a single image. This registration approach allows us to average numerous noisy scanning transmission electron microscopy (STEM) images producing an improved image that surpasses the quality attainable by single shot STEM images.We extend these general ideas to develop a joint registration and denoising approach that allows to match the thorax surface extracted from 3D CT data and intra-fractionally recorded, noisy time-of-flight (ToF) range data. This model helps track intra-fractional respiratory motion with the aim of improving radiotherapy for patients with thoracic, abdominal and pelvic tumors.

Monday, January 30, 2012 - 14:00 ,
Location: Skiles 006 ,
David Mao ,
Institute for Mathematics and Its Applications (IMA) at University of Minnesota ,
Organizer: Sung Ha Kang

Binary function is a class of important function that appears in many applications e.g. image segmentation, bar code recognition, shape detection and so on. Most studies on reconstruction of binary function are based on the nonconvex double-well potential or total variation. In this research we proved that under certain conditions the binary function can be reconstructed from incomplete frequency information by using only simple linear programming, which is far more efficient.

Monday, January 23, 2012 - 14:05 ,
Location: Skiles 006 ,
Alper Erturk ,
Georgia Tech, School of Mechanical Engineering ,
Organizer:

The transformation

of vibrations into low-power electricity has received growing

attention over the last decade. The goal in this research field is to

enable self-powered electronic components by harvesting the

vibrational energy available in their environment. This talk will be

focused on linear and nonlinear vibration-based energy harvesting

using piezoelectric materials, including the modeling and

experimental validation efforts. Electromechanical modeling

discussions will involve both distributed-parameter and

lumped-parameter approaches for quantitative prediction and

qualitative representation. An important issue in energy harvesters

employing linear resonance is that the best performance of the device

is limited to a narrow bandwidth around the fundamental resonance

frequency. If the excitation frequency slightly deviates from the

resonance condition, the power output is drastically reduced. Energy

harvesters based on nonlinear configurations (e.g., monostable and

bistable Duffing oscillators with electromechanical coupling) offer

rich nonlinear dynamic phenomena and outperform resonant energy

harvesters under harmonic excitation over a range of frequencies.

High-energy limit-cycle oscillations and chaotic vibrations in

strongly nonlinear bistable beam and plate configurations are of

particular interest. Inherent material nonlinearities and dissipative

nonlinearities will also be discussed. Broadband random excitation of

energy harvesters will be summarized with an emphasis on stochastic

resonance in bistable configurations. Recent efforts on aeroelastic

energy harvesting as well as underwater thrust and electricity

generation using fiber-based flexible piezoelectric composites will

be addressed briefly.

of vibrations into low-power electricity has received growing

attention over the last decade. The goal in this research field is to

enable self-powered electronic components by harvesting the

vibrational energy available in their environment. This talk will be

focused on linear and nonlinear vibration-based energy harvesting

using piezoelectric materials, including the modeling and

experimental validation efforts. Electromechanical modeling

discussions will involve both distributed-parameter and

lumped-parameter approaches for quantitative prediction and

qualitative representation. An important issue in energy harvesters

employing linear resonance is that the best performance of the device

is limited to a narrow bandwidth around the fundamental resonance

frequency. If the excitation frequency slightly deviates from the

resonance condition, the power output is drastically reduced. Energy

harvesters based on nonlinear configurations (e.g., monostable and

bistable Duffing oscillators with electromechanical coupling) offer

rich nonlinear dynamic phenomena and outperform resonant energy

harvesters under harmonic excitation over a range of frequencies.

High-energy limit-cycle oscillations and chaotic vibrations in

strongly nonlinear bistable beam and plate configurations are of

particular interest. Inherent material nonlinearities and dissipative

nonlinearities will also be discussed. Broadband random excitation of

energy harvesters will be summarized with an emphasis on stochastic

resonance in bistable configurations. Recent efforts on aeroelastic

energy harvesting as well as underwater thrust and electricity

generation using fiber-based flexible piezoelectric composites will

be addressed briefly.

Monday, November 28, 2011 - 14:00 ,
Location: Skiles 006 ,
Christel Hohenegger ,
Mathematics, Univ. of Utah ,
Organizer:

One of the challenges in modeling the transport properties of complex fluids (e.g. many biofluids, polymer solutions, particle suspensions) is describing the interaction between the suspended micro-structure with the fluid itself. Here I will focus on understanding the dynamics of semi-dilute active suspensions, like swimming bacteria or artificial micro-swimmers modeled via a simple kinetic model neglecting chemical gradients and particle collisions. I will then present recent results on the linearized structure of such an active system near a state of uniformity and isotropy and on the onset of the instability as a function of the volume concentration of swimmers, both for a periodic domain. Finally, I will discuss the role of the domain geometry in driving the flow and the large-scale flow instabilities, as well as the appropriate boundary conditions.

Monday, November 14, 2011 - 14:00 ,
Location: Skiles 006 ,
Olof Widlund ,
Courant Institute,New York University, Mathematics and Computer Science ,
Organizer: Haomin Zhou

The domain decomposition methods considered are preconditioned conjugate gradient methods designed for the very large algebraic systems of equations which often arise in finite element practice. They are designed for massively parallel computer systems and the preconditioners are built from solvers on the substructures into whichthe domain of the given problem is partitioned. In addition, to obtain scalability, there must be a coarse problem, with a small number of degrees of freedom for each substructure. The design of this coarse problem is crucial for obtaining rapidly convergent iterations and poses the most interesting challenge in the analysis.Our work will be illustrated by overlapping Schwarz methods for almost incompressible elasticity approximated by mixed finite element and mixed spectral element methods. These algorithms is now used extensively at the SANDIA, Albuquerque laboratories and were developed in close collaboration with Dr. Clark R. Dohrmann. These results illustrate two roles of the coarse component of the preconditioner.Currently, these algorithms are being actively developed for problems posed in H(curl) and H(div). This work requires the development of new coarse spaces. We will also comment on recent work on extending domain decomposition theory to subdomains with quite irregular boundaries. This work is relevant because of the use of mesh partitioners in the decomposition of large finite element matrices.

Monday, November 7, 2011 - 14:00 ,
Location: Skiles 006 ,
Jingfang Liu ,
GT Math ,
Organizer: Sung Ha Kang

The empirical mode decomposition (EMD) was a method developed by Huang et al as an alternative approach to the traditional Fourier and wavelet techniques for studying signals. It decomposes signals into finite numbers of components which have well behaved intataneous frequency via Hilbert transform. These components are called intrinstic mode function (IMF). Recently, alternative algorithms for EMD have been developed, such as iterative filtering method or sparse time-frequency representation by optimization. In this talk we present our recent progress on iterative filtering method. We develop a new local filter based on a partial differential equation (PDE) model as well as a new approach to compute the instantaneous frequency, which generate similar or better results than the traditional EMD algorithm.

Monday, October 31, 2011 - 14:00 ,
Location: Skiles 006 ,
Jie Shen ,
Purdue University, Department of Mathematics ,
Organizer: Haomin Zhou

Many scientific, engineering and financial applications require solving high-dimensional PDEs. However, traditional tensor product based algorithms suffer from the so called "curse of dimensionality".We shall construct a new sparse spectral method for high-dimensional problems, and present, in particular, rigorous error estimates as well as efficient numerical algorithms for elliptic equations in both bounded and unbounded domains.As an application, we shall use the proposed sparse spectral method to solve the N-particle electronic Schrodinger equation.