SCIENTIFIC COMPUTING AND IMAGING INSTITUTE
at the University of Utah

An internationally recognized leader in visualization, scientific computing, and image analysis

SCI Publications

2007


H.T. Vo, S.P. Callahan, N. Smith, C.T. Silva, W. Martin, D. Owen, D.M. Weinstein. “iRun: Interactive Rendering of Large Unstructured Grids,” In Proceedings of the 7th Eurographics Symposium on Parallel Graphics and Visualization (EGPGV 2007), pp. 93--100. 2007.


I. Wald, S. Boulos, P. Shirley. “Ray Tracing Deformable Scenes using Dynamic Bounding Volume Hierarchies,” In ACM Transactions on Graphics, Vol. 26, No. 1, 2007.


I. Wald, H. Friedrich, C.D. Hansen. “Interactive Isosurface Ray Tracing of Time-Varying Tetrahedral Volumes,” SCI Institute Technical Report, No. UUSCI-2007-003, University of Utah, 2007.


I. Wald, H. Friedrich, A. Knoll, C.D. Hansen. “Interactive Isosurface Ray Tracing of Time-Varying Tetrahedral Volumes,” In IEEE Transactions on Visualization and Computer Graphics, Vol. 13, No. 6, pp. 1727--1734. 2007.
DOI: 10.1109/TVCG.2007.70566


I. Wald, C. Gribble, S. Boulos, A. Kensler. “SIMD Ray Stream Tracing - SIMD Ray Traversal with Generalized Ray Packets and On-the-fly Re-Ordering -,” SCI Institute Technical Report, No. UUSCI-2007-012, University of Utah, 2007.


I. Wald, W.R. Mark, J. Günther, S. Boulos, T. Ize, W. Hunt, S.G. Parker, P. Shirley. “State of the Art in Ray Tracing Animated Scenes,” In Proceedings of Eurographics 2007, State of the Art Reports, pp. (accepted). 2007.


I. Wald. “On fast Construction of SAH based Bounding Volume Hierarchies,” In Proceedings of the 2007 Eurographics/IEEE Symposium on Interactive Ray Tracing, pp. 33--40. 2007.


G.H. Weber, P.-T. Bremer, V. Pascucci. “Topological Landscapes: A Terrain Metaphor for Scientific Data,” In IEEE Transactions on Visualization and Computer Graphics, Note: (presented at IEEE VIS 2007), 2007.


G.H. Weber, S.E. Dillard, H. Carr, V. Pascucci, B. Hamann. “Topology-controlled volume rendering,” In IEEE Transactions on Visualization and Computer Graphics, Vol. 13, No. 2, pp. 330--341. January, 2007.


A. Wiebel, X. Tricoche, D. Schneider, Heike Jänicke, Gerik Scheuermann. “Generalized Streak Lines: Analysis and Visualization of Boundary Induced Vortices,” In Proceeding of IEEE Visualization 2007, pp. 1735--1742. 2007.


C.H. Wolters, H. Köstler, C. Möller, J. Härtlein, L. Grasedyck, W. Hackbusch. “Numerical Mathematics of the Subtraction Method for the Modeling of a Current Dipole in EEG Source Reconstruction Using Finite Element Head Models,” In SIAM J. on Scientific Computing, Vol. 30, No. 1, pp. 24--45. 2007.


C.H. Wolters, H. Köstler, C. Möller, J. Härdtlein, A. Anwander. “Numerical Approaches for Dipole Modeling in Finite Element Method Based Source Analysis,” In New Frontiers in Biomagnetism. Proceedings of the 15th International Conference on Biomagnetism, Vancouver, BC, Canada, August 21-25, 2006., International Congress Series, Vol. 1300, pp. 189--192. June, 2007.


D. Xiu, J. Shen. “An Efficient Spectral Method for Acoustic Scattering from Rough Surfaces,” In Communications in Computational Physics, Vol. 2, No. 1, pp. 54--72. 2007.
DOI: 10.1.1.111.815

ABSTRACT

An efficient and accurate spectral method is presented for scattering problems with rough surfaces. A probabilistic framework is adopted by modeling the surface roughness as random process. An improved boundary perturbation technique is employed to transform the original Helmholtz equation in a random domain into a stochastic Helmholtz equation in a fixed domain. The generalized polynomial chaos (gPC) is then used to discretize the random space; and a Fourier-Legendre method to discretize the physical space. These result in a highly efficient and accurate spectral algorithm for acoustic scattering from rough surfaces. Numerical examples are presented to illustrate the accuracy and efficiency of the present algorithm.

Keywords: Acoustic scattering, spectral methods, stochastic inputs, differential equations, uncertainty quantification


D. Xiu. “Efficient Collocational Approach for Parametric Uncertainty Analysis,” In Communications in Computational Physics, Vol. 2, No. 2, pp. 293--309. 2007.

ABSTRACT

A numerical algorithm for effective incorporation of parametric uncertainty into mathematical models is presented. The uncertain parameters are modeled as random variables, and the governing equations are treated as stochastic. The solutions, or quantities of interests, are expressed as convergent series of orthogonal polynomial expansions in terms of the input random parameters. A high-order stochastic collocation method is employed to solve the solution statistics, and more importantly, to reconstruct the polynomial expansion. While retaining the high accuracy by polynomial expansion, the resulting \"pseudo-spectral\" type algorithm is straightforward to implement as it requires only repetitive deterministic simulations. An estimate on error bounded is presented, along with numerical examples for problems with relatively complicated forms of governing equations.

Keywords: Collocation methods, pseudo-spectral methods, stochastic inputs, random differential equations, uncertainty quantification


D. Xiu, S.J. Sherwin. “Parametric Uncertainty Analysis of Pulse Wave Propagation in a Model of a Human Arterial Networks,” In Journal of Computational Physics, Vol. 226, No. 2, pp. 1385--1407. 2007.
DOI: 10.1016/j.jcp.2007.05.020

ABSTRACT

Reduced models of human arterial networks are an efficient approach to analyze quantitative macroscopic features of human arterial flows. The justification for such models typically arise due to the significantly long wavelength associated with the system in comparison to the lengths of arteries in the networks. Although these types of models have been employed extensively and many issues associated with their implementations have been widely researched, the issue of data uncertainty has received comparatively little attention. Similar to many biological systems, a large amount of uncertainty exists in the value of the parameters associated with the models. Clearly reliable assessment of the system behaviour cannot be made unless the effect of such data uncertainty is quantified.

In this paper we present a study of parametric data uncertainty in reduced modelling of human arterial networks which is governed by a hyperbolic system. The uncertain parameters are modelled as random variables and the governing equations for the arterial network therefore become stochastic. This type stochastic hyperbolic systems have not been previously systematically studied due to the difficulties introduced by the uncertainty such as a potential change in the mathematical character of the system and imposing boundary conditions. We demonstrate how the application of a high-order stochastic collocation method based on the generalized polynomial chaos expansion, combined with a discontinuous Galerkin spectral/hp element discretization in physical space, can successfully simulate this type of hyperbolic system subject to uncertain inputs with bounds. Building upon a numerical study of propagation of uncertainty and sensitivity in a simplified model with a single bifurcation, a systematical parameter sensitivity analysis is conducted on the wave dynamics in a multiple bifurcating human arterial network. Using the physical understanding of the dynamics of pulse waves in these types of networks we are able to provide an insight into the results of the stochastic simulations, thereby demonstrating the effects of uncertainty in physiologically accurate human arterial networks.

Keywords: Mathematical biology, Hemodynamics, Arterial network, Stochastic modelling, Uncertainty analysis, High-order methods


Y. Yang, X. Chen, G. Gopalakrishnan, R.M. Kirby. “Distributed Dynamic Partial Order Reduction Based Verification of Threaded Software,” In Proceedings of Model Checking Software: 14th International SPIN Workshop, Berlin, Germany, Vol. 4595/2007, pp. 58--75. July, 2007.


B. Yihnaz, R.S. MacLeod, B.B. Punske, B. Taccardi, and D.H. Brooks. “Generalized training subset selection for statistical estimation of epicardial activation maps from intravenous catheter measurements,” In Compo in BioI. and Med., In Compo in BioI. and Med., Vol. 37, No. 3, pp. 328--336. 2007.


B. Yilmaz, R.S. MacLeod. “Generalized Training Subset Selection for Statistical Estimation of Epicardial Activation Maps from Intravenous Catheter Measurements,” In Computers in Biology and Medicine, Vol. 37, No. 9, pp. 328--336. March, 2007.


F. Zhang, C. Goodlett, E. Hancock, G. Gerig. “Probabilistic White Matter Fiber Tracking using Particle Filtering,” In Proceedings of The 10th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2007), Lecture Notes in Computer Science, Vol. 4791, pp. 144--151. November, 2007.

2006


G. Adluru, E.V.R. DiBella, R.T. Whitaker. “Automatic Segmentation of Cardiac Short Axis Slices in Perfusion MRI,” In Proceedings of The 2006 IEEE International Symposium on Biomedical Imaging, pp. 133--136. 2006.