Angela Violi is a Professor in the Department of Mechanical Engineering, and adjunct faculty in Chemical Engineering, Biophysics, Macromolecular Science and Engineering, and Applied Physics. The research in the group of Violi is focused on the application of statistical mechanics and computational methods to chemically and physically oriented problems in nanomaterials and biology. The group investigates the formation mechanisms of nanomaterials for various applications, including energy and biomedical systems, and the dynamics of biological systems and their interactions with nanomaterials.
Don Siegel is a Professor in the Department of Mechanical Engineering and the Department of Material Science and Engineering. His research targets the discovery, characterization, and understanding of novel materials for energy-related applications. These efforts primarily employ atomic scale modeling to predict thermodynamic properties and kinetics. These data provide the necessary ingredients for identifying performance limiting mechanisms and for the “virtual screening” of candidate compounds having desired properties. Prof. Siegel is currently exploring several varieties of energy storage materials, lightweight structural alloys, and materials suitable for use in carbon capture applications.
Jeff Fessler is a Professor in the Department of Electrical Engineering and Computer Science – Electrical and Computer Engineering Division. His research interests include numerical optimization, inverse problems, image reconstruction, computational imaging, tomography, magnetic resonance imaging. Most of these applications involve large problem sizes and parallel computing methods (cluster, cloud, GPU, SIMD, etc.) are needed.
Thornton’s research focuses on computational and theoretical investigations of the evolution of microstructures and nanostructures during processing and operation of materials. These investigations facilitate the understanding of the underlying physics of materials and their performance, which will aid us in designing advanced materials with desirable properties and in developing manufacturing processes that would enable their fabrication. The topics include growth and coarsening of precipitates, evolution of morphologically and topologically complex systems, microstructure-based simulations of electrochemical systems such as batteries, and self-assembly of quantum dots and other nanoscale phenomena during heteroepitaxy of semiconductors. These projects involve advanced computational methods and large-scale simulations performed on high-performance computational platforms, and insights provide a means for material design and optimization.
His research interests lie in the development of numerical methods and models for massively parallel computations of fluid mechanics problems on modern computing architectures, including GPUs. He specifically focuses on high-order accurate finite difference/volume/element and spectral methods desgined for robust, accurate and efficient simulations. With his codes, he investigates the basic physics of multiphase flows, high-speed flows and shock waves, turbulence and mixing, interfacial instabilities, complex fluids and plasmas. Target applications include biomedical engineering, energy, aeronautics and naval engineering.
His group uses first-principles computational methods and high-performance computing resources to predictively model the structural, electronic, and optical properties of bulk materials and nanostructures. The goal is to understand, predict, and optimize the properties of novel electronic, optoelectronic, photovoltaic, and thermoelectric materials.
Brian Arbic is an Associate Professor in the Department of Earth and Environmental Sciences, with an appointment in the Department of Climate and Space Sciences Engineering and affiliations with Applied and Interdisciplinary Mathematics, Applied Physics, and the Center for the Study of Complex Systems. Arbic is a physical oceanographer primarily interested in the dynamics and energy budgets of oceanic mesoscale eddies (the oceanic equivalent of atmospheric weather systems), the large-scale oceanic general circulation, and tides. He has also studied paleotides, tsunamis, and the decadal variability of subsurface ocean temperatures and salinities. His primary tools are numerical models of the ocean. Arbic uses both realistic models, such as the HYbrid Coordinate Ocean Model (HYCOM) being used as a U.S. Navy ocean forecast model, and idealized models. He frequently compares the outputs of such models to oceanic observations, taken with a variety of instruments. Comparison of models and observations helps us to improve models and ideas about how the ocean works. His research has often been interdisciplinary, involving collaborations with scientists outside of my discipline, such as glaciologists, geodynamicists, and marine geophysicists.
Charles L. Brooks III is the Warner-Lambert/Parke-Davis Professor of Chemistry and a Professor of Biophysics. He is affiliated with the department of Chemistry, Biophysics Program, program in Applied Physics, Molecular Biophysics Training Program (Director), program in Chemical Biology, Bioinformatics Graduate Program, Center for Computational Medicine and Bioinformatics and the Medicinal Chemistry Interdepartmental Graduate Program. The research in the group of Charles L. Brooks III is focused on the application of statistical mechanics, quantum chemistry and computational methods to chemically and physically oriented problems in biology. The group develops and applies computational models to studies of the dynamics of proteins, nucleic acids and their complexes, including virus structure and assembly. They specifically develop novel computational methods for the inclusion of pH effects in modeling biological systems. Significant focus is in the development of a large, world-wide distributed software package for molecular simulations, CHARMM. Efforts are ongoing to explore new means of parallel and accelerated computation utilizing scalable parallel algorithms for molecular dynamics and integrated CPU/GPU computational models.