Designed especially for neurobiologists, FluoRender is an interactive tool for multi-channel fluorescence microscopy data visualization and analysis.
Deep brain stimulation
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.

SCI Publications

2006


 C.E. Goodyer, M. Berzins. “Adaptive Timestepping for Elastohydrodynamic Lubrication Solvers,” In SIAM Journal on Scientific Computing, Vol. 28, No. 2, SIAM, pp. 626-650. 2006.
DOI: 10.1137/050622092


C. Gribble, T. Ize, A. Kensler, I. Wald, S.G. Parker. “A Coherent Grid Traversal Approach to Visualizing Particle-based Simulation Data,” SCI Institute Technical Report, No. UUSCI-2006-024, University of Utah, 2006.


C.P. Gribble, A.J. Stephens, J.E. Guilkey, S.G. Parker. “Visualizing Particle-Based Simulation Datasets on the Desktop,” In Proceedings of the British HCI 2006 Workshop on Combining Visualization and Interaction to Facilitate Scientific Exploration and Discovery, 2006.


C.P. Gribble, S.G. Parker. “Enhancing Interactive Particle Visualization with Advanced Shading Models,” In Proceedings of the ACM Siggraph Third Symposium on Applied Perception in Graphics and Visualization, pp. 111--118. 2006.

ABSTRACT

Particle-based simulation methods are used to model a wide range of complex phenomena and to solve time-dependent problems of various scales. Effective visualization of the resulting state should communicate subtle changes in the three-dimensional structure, spatial organization, and qualitative trends within a simulation as it evolves. We take steps toward understanding and using advanced shading models in the context of interactive particle visualization. Specifically, the impact of ambient occlusion and physically based diffuse interreflection is investigated using a formal user study. We find that these shading models provide additional visual cues that enable viewers to better understand subtle features within particle datasets. We also describe a visualization process that enables interactive navigation and exploration of large particle datasets, rendered with illumination effects from advanced shading models. Informal feedback from application scientists indicates that the results of this process enhance the data analysis tasks necessary for understanding complex particle datasets.

Keywords: mpm material point method, rtrt real-time ray tracing, csafe c-safe, rtcenter


 J.E. Guilkey, J.B. Hoying, J.A. Weiss. “Computational Modeling of Multicellular Constructs with the Material Point Method,” In Journal of Biomechanics, Vol. 39, No. 11, pp. 2074--2086. 2006.


Y. Guo, J.A. Nairn. “Three-Dimensional Dynamic Fracture Analysis using the Material Point Method,” In Computer Modeling in Engineering and Sciences, Vol. 1, No. 1, pp. 11--25. 2006.

ABSTRACT

This paper describes algorithms for three-dimensional dynamic stress and fracture analysis using the material point method (MPM). By allowing dual velocity fields at background grid nodes, the method provides exact numerical implementation of explicit cracks in a predominantly meshless method. Crack contact schemes were included for automatically preventing crack surfaces from interpenetration. Crack-tip parameters, dynamic J-integral vector and mode I, II, and III stress intensity factors, were calculated from the dynamic stress solution. Comparisons to finite difference method (FDM), finite element method (FEM), and boundary element method (BEM), as well as to static theories showed that MPM can efficiently and accurately solve three-dimensional dynamic fracture problems. Since the crack description is independent of the object description, MPM could be useful for simulation of three-dimensional dynamic crack propagation in arbitrary directions.


 A. Gyulassy, V. Natarajan, V. Pascucci, P.-T. Bremer, B. Hamann. “A Topological Approach to Simplification of Three-Dimensional Scalar Functions,” In IEEE Transactions on Visualization and Computer Graphics, Vol. 12, No. 4, pp. 474--484. August, 2006.


C.W. Hamman, R.M. Kirby, J.C. Klewicki. “On the Lamb Vector Divergence as a Momentum Field Diagnostic Employed in Turbulent Channel Flow,” In Proceedings of the 59th Annual Meeting of the American Physical Society, Division of Fluid Dynamics, Tampa Bay, Fl, November, 2006.


D. Hart, C.E. Goodyer, M. Berzins, P.K. Jimack, L. Scales. “Adjoint Error Estimation and Spatial Adaptivity for EHL-Like Models,” In IUTAM Symposium on Elastohydrodynamics and Micro-elastohydrodynamics, Springer, pp. 47--58. 2006.
DOI: 10.1007/1-4020-4533-6_3


H.C. Hazlett, M.D. Poe, G. Gerig, R.G. Smith, J. Piven. “Cortical Gray and White Brain Tissue Volume in Adolescents and Adults with Autism,” In Biological Psychiatry, Vol. 59, No. 1, pp. 1--96. January, 2006.


I. Ionescu, J.E. Guilkey, M. Berzins, R.M. Kirby, J.A. Weiss. “Simulation of Soft Tissue Failure Using the Material Point Method,” In Journal of Biomechanical Engineering, Vol. 128, No. 6, pp. 917--924. 2006.


T. Ize, I. Wald, C. Robertson, S.G. Parker. “An Evaluation of Parallel Grid Construction for Ray Tracing Dynamic Scenes,” SCI Institute Technical Report, No. UUSCI-2006-025, University of Utah, 2006.


T. Ize, I. Wald, C. Robertson, S.G. Parker. “An Evaluation of Parallel Grid Construction for Ray Tracing Dynamic Scenes,” In Proceedings of the 2006 IEEE Symposium on Interactive Ray Tracing, Vol. 1, pp. 47--55. 2006.


T. Ize, I. Wald, S.G. Parker. “Asynchronous BVH Construction for Ray Tracing Dynamic Scenes,” Note: UUSCI-2006-034, SCI Institute, 2006.


W-K. Jeong, R.T. Whitaker, M. Dobin. “Interactive 3D Seismic Fault Detection on the Graphics Hardware,” In Proceedings of the 2006 International Workshop on Volume Graphics, In Proceedings of International Workshop on Volume Graphics, pp. 111--118. 2006.


C.R. Johnson, D.M. Weinstein. “Biomedical Computing and Visualization,” In Proceedings of the Twenty-Ninth Australasian Computer Science Conference (ACSC2006): Conferences in Research and Practice in Information Technology (CRPIT), Hobart, Australia, Vol. 48, Edited by Vladimir Estivill-Castro and Gill Dobbie, pp. 3-10. 2006.


C.R. Johnson, R. Moorhead, T. Munzner, H. Pfister, P. Rheingans, T. S. Yoo. “NIH-NSF Visualization Research Challenges Report,” Note: http://tab.computer.org/vgtc/vrc/index.html, IEEE Press, 2006.
ISBN: 0-7695-2733-7


J.T. Johnson III, M.S. Hansen, I. Wu, L.J. Healy, C.R. Johnson, G.M. Jones, M.R. Capecchi, C. Keller. “Virtual Histology of Transgenic Mouse Embryos for High-Throughput Phenotyping,” In PLoS Genetics, Vol. 2, No. 1, pp. 471--477. April, 2006.


C.R. Johnson. “Computational Methods and Software for Bioelectric Field Problems,” In Biomedical Engineering Handbook, 2nd Edition, Vol. 1, Ch. 23, Edited by J.D. Bronzino, CRC Press, Boca Raton, pp. 1--23. 2006.


C.R. Johnson, S.G. Parker. “Problem Solving Environments for DDDAS,” In Proceedings of the Ninth Copper Mountain Conference on Iterative Methods, Copper Mountain, CO, Note: Minisymposium: Actually Doing Dynamic Data-driven Application Simulations, April, 2006.