Designed especially for neurobiologists, FluoRender is an interactive tool for multi-channel fluorescence microscopy data visualization and analysis.
Large scale visualization on the Powerwall.
BrainStimulator is a set of networks that are used in SCIRun to perform simulations of brain stimulation such as transcranial direct current stimulation (tDCS) and magnetic transcranial stimulation (TMS).
Developing software tools for science has always been a central vision of the SCI Institute.

Scientific Computing

Numerical simulation of real-world phenomena provides fertile ground for building interdisciplinary relationships. The SCI Institute has a long tradition of building these relationships in a win-win fashion – a win for the theoretical and algorithmic development of numerical modeling and simulation techniques and a win for the discipline-specific science of interest. High-order and adaptive methods, uncertainty quantification, complexity analysis, and parallelization are just some of the topics being investigated by SCI faculty. These areas of computing are being applied to a wide variety of engineering applications ranging from fluid mechanics and solid mechanics to bioelectricity.


martin

Martin Berzins

Parallel Computing
GPUs
mike

Mike Kirby

Finite Element Methods
Uncertainty Quantification
GPUs
pascucci

Valerio Pascucci

Scientific Data Management
chris

Chris Johnson

Problem Solving Environments
ross

Ross Whitaker

GPUs
chuck

Chuck Hansen

GPUs
       

Scientific Computing Project Sites:


Publications in Scientific Computing:


Coarse Resolution Turbulence Simulations With Spectral Vanishing Viscosity - Large-Eddy Simulations (SVV-LES)
R.M. Kirby, G.E. Karniadakis. In Journal of Fluids Engineering, Vol. 124, No. 4, pp. 886--891. 2002.



Can a Spherical Model Substitute for a Realistic Head Model in Forward and Inverse MEG Simulations?
R. Van Uitert, C.R. Johnson. In Proceedings of The 13th International Conference on Biomagnetism, Jena, Germany, August, 2002.



Component-Based Problem Solving Environments for Large-Scale Scientific Computing
C.R. Johnson, S.G. Parker, D. Weinstein, S. Heffernan. In J. Conc. & Comp.: Prac. & Exper., Vol. 14, pp. 1337--1349. 2002.



Modeling and Simulation in Medicine: Towards an Integrated Framework
G. Higgins, B. Athey, J. Bassingthwaighte, J. Burgess, H. Champion, K. Cleary, P. Dev, J. Duncan, M. Hopmeier, D. Jenkins, C.R. Johnson, H. Kelly, R. Leitch, W. Lorensen, D. Metaxas, V. Spitzer, N. Vaidehi, K. Vosburgh, R. Winslow. In Computer Aided Surgery, Vol. 6, No. 1, Note: Final report of the meeting of the same title held July 20-21, 2000, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA., 2001.
DOI: 10.1002/igs.1008



Grid-enabling Problem Solving Environments: A Case Study of SCIRun and Netsolv
M. Miller, C. Moulding, J. Dongarra, C.R. Johnson. In Proceedings of The 5th International Conference on High-Performance Computing, 2001 Advanced Simulation Technologies Conference, Society for Modeling and Simulation International, pp. 98--103. April, 2001.



Multilevel Methods for Inverse Bioelelectric Field Problems
C.R. Johnson, M. Mohr, U. Ruede, A. Samsonov, K. Zyp. In Lecture Notes in Computational Science and Engineering - Multiscale and Multiresolution Methods: Theory and Applications, Vol. 20, Edited by T.J. Barth and T.F. Chan and R. Haimes, Springer-Verlag Publishing, Heidelberg pp. 331--346. October, 2001.



Computational Engineering and Science Program at the University of Utah
C. DeTar, A.L. Fogelson, C.R. Johnson, C.A. Sikorski. In Proceedings of the International Conference on Computational Science (ICCS) 2001, San Francisco, Edited by V. Alexandrov and J. Dongarra and B. Juliano and R. Renner and K. Tan, pp. 1176--1185. May, 2001.



Finite Element EEG and MEG Simulations for Realistic Head Models: Quadratic vs. Linear Approximations
R. Van Uitert, D. Weinstein, C.R. Johnson, L. Zhukov. In Biomed. Technik, Vol. 46, pp. 32--34. 2001.



Integrated Simulation for MEMS: Coupling Flow-Structure-Thermal-Electrical Domains
R.M. Kirby, G.E. Karniadakis, O. Mikulchenko, K. Mayaram. In The MEMS Handbook, Edited by M. Gad-el-Hak, Informa UK Limited, 2001.



Adaptive Finite Element and Local Regularization Methods for the Inverse ECG Problem
C.R. Johnson. In Inverse Problems in Electrocardiology, Advances in Computational Biomedicine, Vol. 5, Edited by Peter Johnston, WIT Press, pp. 51--88. 2001.



An Integrated Simulator for Coupled Domain Problems in MEMS
R.M. Kirby, G.E. Karniadakis, O. Mikulchenko, K. Mayaram. In Journal of Microelectromechanical Systems, Vol. 10, No. 3, pp. 379--399. 2001.



The Uintah Parallelism Infrastructure: A Performance Evaluation on the SGI Origin 2000
J. McCorquodale, J.D. de St. Germain, S.G. Parker, C.R. Johnson. In Proceedings of The 5th International Conference on High-Performance Computing, Seattle, Mar, 2001.



Under-Resolution and Diagnostics in Spectral Simulations of Complex-Geometry Flows
R.M. Kirby, G.E. Karniadakis. In Turbulent Flow Computation, Edited by D. Drikakis and B. Geurts, Springer, pp. 1--42. 2001.



Parallelization and Integration of Fire Simulations in the Uintah PSE
R. Rawat, S.G. Parker, P.J. Smith, C.R. Johnson. In Proceedings of the Tenth SIAM Conference on Parallel Processing for Scientific Computing, Portsmouth, Virginia, March 12-14, 2001.



An In-depth Investigation of the Multigrid Approach to Steady and Transient EHL Problems
C.E. Goodyer, R. Fairlie, M. Berzins, L.E. Scales. In Thinning Films and Tribological InterfacesProceedings of the 26th Leeds-Lyon Symposium on Tribology, Tribology Series, Vol. 38, Edited by D. Dowson, M. Priest, C.M. Taylor, P. Ehret, T.H.C. Childs, G. Dalmaz, A.A. Lubrecht, Y. Berthier, L. Flamand and J.-M. Georges, Elsevier, pp. 95--102. 2000.
ISSN: 0167-8922

Multigrid methods have proved robust and highly desirable in terms of the iteration speed in solving elastohydrodynamic lubrication (EHL) problems. Lubrecht, Venner and Ehret, amongst others, have shown that multigrid can be successfully used to obtain converged solutions for steady problems. steady problems.

A detailed study reinforces these results but also shows, in some cases, that while multigrid techniques give initial rapid convergence, the residuals - having dropped to a low level - may reach a stalling point, mainly due to the cavitation region. The study will explain this behaviour in terms of the iterative scheme and show how, if this happens, the errors in the fine grid solution can be reduced further. Example results of both steady and transient EHL problems (including a thermal viscoelastic case) are shown with further developments into adaptive meshes considered.



An in-depth investigation of the multigrid approach to steady and transient EHL problems,
C.E. Goodyera, R. Fairliea, M. Berzinsa, L.E. Scales. In 26th Leeds-Lyon Symposium on Tribology, Vol. 38, Elsevier, pp. 95-102. 2000.

Multigrid methods have proved robust and highly desirable in terms of the iteration speed in solving elastohydrodynamic lubrication (EHL) problems. Lubrecht, Venner and Ehret, amongst others, have shown that multigrid can be successfully used to obtain converged solutions for steady problems, steady problems. A detailed study reinforces these results but also shows, in some cases, that while multigrid techniques give initial rapid convergence, the residuals - having dropped to a low level - may reach a stalling point, mainly due to the cavitation region. The study will explain this behaviour in terms of the iterative scheme and show how, if this happens, the errors in the fine grid solution can be reduced further. Example results of both steady and transient EHL problems (including a thermal viscoelastic case) are shown with further developments into adaptive meshes considered. © 2000 Elsevier Science B.V. All rights reserved.



Dynamic Data Driven Application Systems: Creating a dynamic and symbiotic coupling of application/simulations with measurements/experiments
A. Deshmukh, C.C. Douglas, M. Ball, R.E. Ewing, C.R. Johnson, C. Kesselman, C. Lee. Note: 28 pages, Edited by W. Powell, R. Sharpley, National Science Foundation, 2000.



Workshop on Scientific Knowledge, Information and Computing, SIDEKIC 98
R. Bramley, C.R. Johnson, D. Gannon, J. Reynders, T. Hewett, J. Rice. In Enabling Technologies for Computational Science, Springer, pp. 19-32. 2000.
DOI: 10.1007/978-1-4615-4541-5_2

On 4-5 December 1998 researchers from several universities, national laboratories, software companies, and government funding agencies met at Santa Fe, NM for the 1998 Scientific Integrated Development Environments for Knowledge, Information, and Computing Workshop. The purpose of this meeting was to summarize the state-of-the-art in the area of problem-solving environments (PSEs) for scientific and engineering computation, and to map out directions for future research in the area. This report presents some of the results from the meeting and recommends promising areas for further work. This report begins with a justification of the need for PSEs, which are also commonly called computational workbenches. Next a listing of characteristics that many PSEs share is presented, followed by a small sample listing of current systems. Design goals and future directions, with an emphasis on research issues, are outlined, followed by summary findings and conclusions.



An Inverse EEG Problem Solving Environment and its Applications to EEG Source Localization
D.M. Weinstein, L. Zhukov, C.R. Johnson. In NeuroImage (suppl.), pp. 921. 2000.



Computational Steering and the SCIRun Integrated Problem Solving Environment
S.G. Parker, M. Miller, C.D. Hansen, C.R. Johnson. In Proceedings of Dagstuhl 1997 Workshop on Scientific Visualization, Note: Invited and peer reviewed, Edited by Hans Hagen and Greg Nielson and Frits Post, pp. 257--266. 2000.