News Article | February 16, 2017
Abstract: Francis (Frank) Alexander, a physicist with extensive management and leadership experience in computational science research, has been named Deputy Director of the Computational Science Initiative at the U.S. Department of Energy's (DOE) Brookhaven National Laboratory, effective February 1. Alexander comes to Brookhaven Lab from DOE's Los Alamos National Laboratory, where he was the acting division leader of the Computer, Computational, and Statistical Sciences (CCS) Division. During his more than 20 years at Los Alamos, he held several leadership roles, including as leader of the CCS Division's Information Sciences Group and leader of the Information Science and Technology Institute. Alexander first joined Los Alamos in 1991 as a postdoctoral researcher at the Center for Nonlinear Studies. He returned to Los Alamos in 1998 after doing postdoctoral work at the Institute for Scientific Computing Research at DOE's Lawrence Livermore National Laboratory and serving as a research assistant professor at Boston University's Center for Computational Science. "I was drawn to Brookhaven by the exciting opportunity to strengthen the ties between computational science and the significant experimental facilities-the Relativistic Heavy Ion Collider, the National Synchrotron Light Source II, and the Center for Functional Nanomaterials [all DOE Office of Science User Facilities]," said Alexander. "The challenge of getting the most out of high-throughput and data-rich science experiments is extremely exciting to me. I very much look forward to working with the talented individuals at Brookhaven on a variety of projects, and am grateful for the opportunity to be part of such a respected institution." In his new role as deputy director, Alexander will work with CSI Director Kerstin Kleese van Dam to expand CSI's research portfolio and realize its potential in data-driven discovery. He will serve as the primary liaison to national security agencies, as well as develop strategic partnerships with other national laboratories, universities, and research institutions. His current research interest is the intersection of machine learning and physics (and other domain sciences). "We are incredibly happy that Frank decided to join our CSI team," said Kleese van Dam. "With his background in high-performance computing, data science, and computational and statistical physics, he is the ideal fit for Brookhaven." Throughout his career, Alexander has worked in a variety of areas, including nonequilibrium physics and computational physics. More recently, he has focused on the optimal design of experiments as part of the joint DOE/National Cancer Institute collaboration on cancer research, as well as on uncertainty quantification and error analysis for the prediction of complex systems' behavior. Alexander has served on many committees and advisory panels, including those related to DOE's Laboratory Directed Research and Development [http://science.energy.gov/lp/laboratory-directed-research-and-development/] program. Currently, he is on DOE's Computational Research Leadership Council and the editorial board of Computing in Science & Engineering Magazine. Alexander received his PhD in physics in 1991 from Rutgers University and a BS in mathematics and physics in 1987 from The Ohio State University. About Brookhaven National Laboratory Brookhaven National Laboratory is supported by the Office of Science of the U.S. Department of Energy. The Office of Science is the single largest supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time. For more information, please click If you have a comment, please us. Issuers of news releases, not 7th Wave, Inc. or Nanotechnology Now, are solely responsible for the accuracy of the content.
Eckert S.,Helmholtz Center Dresden |
Nikrityuk P.A.,TU Bergakademie Freiberg |
Willers B.,Helmholtz Center Dresden |
Rabiger D.,Helmholtz Center Dresden |
And 6 more authors.
European Physical Journal: Special Topics | Year: 2013
In this minireview, we summarize experimental and numerical studies particularly concerned with applications of rotating magnetic fields (RMF) or travelling magnetic fields (TMF) to directional solidification of metal alloys. After introducing some fundamentals of electromagnetic stirring, we review the insights gained into flow-induced modifications of microstructure and the formation of freckles and macrosegregations. We further discuss recent strategies, using time-modulated RMF and TMF, which aim to overcome the deficiencies of conventional stirring, in particular flow-induced macrosegregation, by effectively controlling the flow field. On the microscale, we show that time-varying flows are able to alter the sidebranch characteristics vital to the potential of fragmentation. © 2013 EDP Sciences and Springer.
Fortmeier O.,Institute for Scientific Computing |
Fortmeier O.,RWTH Aachen |
Martin Bucker H.,Institute for Scientific Computing |
Martin Bucker H.,RWTH Aachen
Journal of Computational Physics | Year: 2011
Level set functions are employed to track interfaces in various application areas including simulation of two-phase flows and image segmentation. Often, a re-initializing algorithm is incorporated to transform a numerically instable level set function to a signed distance function. In this note, we present a parallel algorithm for re-initializing level set functions on unstructured, three-dimensional tetrahedral grids. The main idea behind this new domain decomposition approach is to combine a parallel brute-force re-initializing algorithm with an efficient way to compute distances between the interface and grid points. Time complexity and error analysis of the algorithm are investigated. Detailed numerical experiments demonstrate the accuracy and scalability on up to 128 processes. © 2011 Elsevier Inc.
Beer T.,Institute for Scientific Computing |
Garbereder G.,Institute for Scientific Computing |
Meisen T.,RWTH Aachen |
Reinhard R.,RWTH Aachen |
Kuhlen T.,Institute for Scientific Computing
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2011
Integrative simulation methods are used in engineering sciences today for the modeling of complex phenomena that cannot be simulated or modeled using a single tool. For the analysis of result data appropriate multi dataset visualization tools are needed. The inherently strong relations between the single datasets that typically describe different aspects of a simulated process (e.g. phenomena taking place at different scales) demand for special interaction metaphors, allowing for an intuitive exploration of the simulated process. This work focuses on the temporal aspects of data exploration. A multi level time model and an appropriate interaction metaphor (the Dataset Sequencer) for the interactive arrangement of datasets in the time domain of the analysis space is described. It is usable for heterogeneous display systems ranging from standard desktop systems to immersive multi-display VR devices. © 2011 Springer-Verlag.