The Computational Materials Science Lab, directed by Guofeng Wang, PhD, focuses on developing and applying multiscale computational methods for the acceleration, achievement, and amplification of scientific discoveries in material science. The employed multiscale computational methods include electronic structure calculation, atomistic dynamic modeling, and finite element analysis. Current research projects in the lab include (a) developing novel electro-catalysts for polymer electrolyte membrane fuel cells, (b) simulating surface segregation phenomena in various alloy systems, (c) modeling mechanical deformation process in nanomaterials, (d) investigating material failure mechanisms in rechargeable Li-ion battery, and (e) studying the structure/property relation of polymer materials. In the current laboratory, there are eight Dell Precision quad-core workstations for code development and software evaluation. Moreover, the lab can access the high performance computing clusters managed by the Center for Simulation & Modeling at the University of Pittsburgh.
The Computational Optimization Laboratory contains state-of-the-art computing facilities including several optimization software packages. The laboratory is used for applied research thrusts as well as course instruction. Techniques employed include linear and mixed-integer programming, network flows, nonlinear programming, stochastic programming, Markov decision processes, and heuristic optimization. The applications include medical decision making, facility layout, energy modeling, supply chain management and scheduling. The goals of this laboratory include applying optimization techniques to industrial problems, developing new algorithms for solving specially-structured problems, and teaching at the undergraduate and graduate levels.
Primary research interest of the Computational Solid Mechanics Laboratory, led by Spandan Maiti, is predictive modeling and simulation of fracture and failure of complex materials. We study the evolution and ultimate failure of these materials operating in a multi-physics milieu. A general objective of our research is to provide quantitative descriptions of the relationship between the measurable features of the microstructure of materials systems and their macroscopic failure response. We employ a full suite of experimentally validated theoretical and numerical tools to achieve this feat. We develop advanced theoretical techniques, numerical algorithms, and novel computational frameworks to conduct large-scale modeling and simulation of materials response. The emphasis of our research is on the investigation and prediction of physical aspects of materials behavior, for which we often need to develop novel numerical techniques. Our lab is well equipped with state of the art hardware and software facilities, and is located at the Biotech Center of the University of Pittsburgh. Currently our research activities span two application areas: A) Biomechanical behavior of native tissues and biodevices and B) Electro-chemo-mechanical response of advanced energy storage materials. Our effort is directed to unlock fundamental mechanisms responsible for damage, tear, and ultimate failure of these complex materials subjected to not only normal, but also altered multi-physics operating environment.
The primary objective of the Computational Transport Phenomena Laboratory, under the direction of Peyman Givi, PhD, is to conduct theoretical research in fluid mechanics, combustion, heat and mass transfer, applied mathematics, and numerical methods. The emphasis of current research in this laboratory is on "understanding physics" rather than "developing numerical algorithms."
Several areas of current investigations are turbulent mixing, chemically reacting flows, high-speed combustion and propulsion, transition and turbulence, nano-scale heat transfer, magnetohydrodynamics, and plasma physics. The numerical methodologies in use consist of spectral methods (collocation, Galerkin), variety of finite difference, finite volume and finite element schemes, Lagrangian methods, and many hybrid methods such as spectral-finite element and spectral-finite difference schemes. The laboratory is equipped with high-speed mini-supercomputers, graphic systems, and state-of-the-art hardware and software for "flow visualization." Most computations require the use of off-site supercomputers (mostly parallel platforms), for which high-speed links are available.
The Computer Architecture Laboratory in Electrical and Computer Engineering and under the direction of Jun Yang, PhD, is a research laboratory devised to investigate advanced computer microarchitectures, computer system architecture, power/thermal management in computer systems, multi-core microprocessors, memory systems, emerging memory technologies, interconnection networks, 3D integration and hardware security. The lab is equipped with networked high-end multi-processor Linux servers, over 10TB mass network storage and solid state drivers, testing motherboards, and more than a dozen Windows and Linux workstations. The laboratory software consists of state-of-the-art simulation tools from both public domains and in-house developed simulation warehouse. The laboratory is sponsored by NSF, SSOE, and Intel Corporation.
The Computer Laboratory for Innovation and Productivity (CLIP) is a state-of-the-art laboratory that provides IE students access to state-of-the-art industrial engineering software. It allows them to work on projects and enable them to succeed and excel when they join the global workforce. In addition to general University and School software, the lab offers Computer Aided Design, Database, and Productivity Analysis software to students. The Lab mirrors the Holzman Learning Center and allows students to work off-hours on homework and projects.
The Laboratory for Computer Vision and Pattern Recognition in the Department of Electrical and Computer Engineering, headed by Chin-Chung Li, PhD, supports research in computer vision, pattern recognition, machine learning, and image processing. Current research interests include computer-aided classification of prostate cancer cell images, wavelet-based image super-resolution, and applications of diffusion wavelet to multi-resolution information processing. The laboratory is equipped with PC-based image processing and pattern recognition workstations with associated cameras.