SCIENTIFIC COMPUTING AND IMAGING INSTITUTE
at the University of Utah

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

SCI Publications

2005


T. Tasdizen, R.T. Whitaker, R. Marc, B. Jones. “Automatic Correction of Non-uniform Illumination in Transmission Electron Microscopy Images,” SCI Institute Technical Report, No. UUSCI-2005-007, University of Utah, 2005.


T. Terriberry, S. Joshi, G. Gerig. “Hypothesis Testing with Nonlinear Shape Models,” In Information Processing in Medical Imaging (IPMI), Edited by G Christensen and M Sonka, pp. 15--26. July, 2005.


X. Tricoche, C. Garth, G. Scheuermann. “Fast and Robust Extraction of Separation Line Features,” In Scientific Visualization: The Visual Extraction of Knowledge from Data, Edited by G.-P. Bonneau and T. Ertl and G.M. Nielson, Springer, pp. 249--264. 2005.


D. Uesu, L. Bavoil, S. Fleishman, J. Shepherd, C.T. Silva. “Simplication of Unstructured Tetrahedral Meshes by Point Sampling,” In Proceedings of the 2005 International Workshop on Volume Graphics, pp. 157--238. 2005.


A.I. Veress, G.T. Gullberg, J.A. Weiss. “Measurement of Strain in the Left Ventricle with Cine-MRI and Deformable Image Registration,” In ASME J. Biom. Eng., Vol. 127, No. 7, pp. 1195--1207. July 21, 2005.


I. Wald, C. Benthin, A. Efremov, T. Dahmen, J. Gunther, A. Dietrich, V. Havran, H. Seidel, P. Slusallek. “A Ray Tracing based Virtual Reality Framework for Industrial Design,” SCI Institute Technical Report, No. UUSCI-2005-009, University of Utah, 2005.


I. Wald. “DIRmaps : Discretized Incident Radiance Maps for High-Quality Global Illumination Walkthroughs in Complex Environments,” SCI Institute Technical Report, No. UUSCI-2005-010, University of Utah, 2005.


D.M. Weinstein, S.G. Parker, J. Simpson, K. Zimmerman, G.M. Jones. “Visualization in the SCIRun Problem-Solving Environment,” In The Visualization Handbook, Edited by C.D. Hansen and C.R. Johnson, Elsevier, pp. 615--632. 2005.
ISBN: 0-12-387582-X


J.A. Weiss, B.J. Maakestad. “Permeability of Human Medial Collateral Ligament in Compression Transverse to the Collagen Fiber Direction,” In Journal of Biomechanics, Vol. 39, No. 2, pp. 276--283. 2005.


J.A. Weiss, J.C. Gardiner, B.J. Ellis, T.J. Lujan, N.S. Phatak. “Three-Dimensional Finite Element Modeling of Ligaments: Technical Aspects,” In Medical Engineering and Physics, Vol. 27, No. 10, Note: Invited paper for special issue: Advances in the Finite Element Modeling of Soft Tissue Deformation, pp. 845--861. May 21, 2005.


R.T. Whitaker. “Isosurfaces and Level-Sets,” In The Visualization Handbook, Edited by C.D. Hansen and C.R. Johnson, Elsevier, pp. 97--123. 2005.
ISBN: 0-12-387582-X


C. H. Wolters, A. Anwander, X. Tricoche, S. Lew, C.R. Johnson. “Influence of Local and Remote White Matter Conductivity Anisotropy for a Thalamic Source on EEG/MEG Field and Return Current Computation,” In Int.Journal of Bioelectromagnetism, Vol. 7, No. 1, pp. 203--206. 2005.


D. Xiu, S.J. Sherwin, S. Dong, G.E. Karniadakis. “Strong and Auxiliary Forms of the Semi-Lagrangian Method for Incompressible Flows,” In Journal of Scientific Computing, Vol. 25, No. 1-2, pp. 323-346. 2005.
DOI: Journal of Scientific Computing

ABSTRACT

We present a review of the semi-Lagrangian method for advection–diffusion and incompressible Navier–Stokes equations discretized with high-order methods. In particular, we compare the strong form where the departure points are computed directly via backwards integration with the auxiliary form where an auxiliary advection equation is solved instead; the latter is also referred to as Operator Integration Factor Splitting (OIFS) scheme. For intermediate size of time steps the auxiliary form is preferrable but for large time steps only the strong form is stable.

Keywords: Semi-Lagrangian method, spectral element method, incompressible flow


D. Xiu, I.G. Kevrekidis. “Equation-free, Multiscale Computation for Unsteady Random Diffusion,” In SIAM Journal on Multiscale Modeling and Simulation, Vol. 4, No. 3, pp. 915--935. 2005.
DOI: 10.1137/040615006

ABSTRACT

We present an \"equation-free\" multiscale approach to the simulation of unsteady diffusion in a random medium. The diffusivity of the medium is modeled as a random field with short correlation length, and the governing equations are cast in the form of stochastic differential equations. A detailed fine-scale computation of such a problem requires discretization and solution of a large system of equations and can be prohibitively time consuming. To circumvent this difficulty, we propose an equation-free approach, where the fine-scale computation is conducted only for a (small) fraction of the overall time. The evolution of a set of appropriately defined coarse-grained variables (observables) is evaluated during the fine-scale computation, and \"projective integration\" is used to accelerate the integration. The choice of these coarse variables is an important part of the approach: they are the coefficients of pointwise polynomial expansions of the random solutions. Such a choice of coarse variables allows us to reconstruct representative ensembles of fine-scale solutions with \"correct\" correlation structures, which is a key to algorithm efficiency. Numerical examples demonstrating accuracy and efficiency of the approach are presented.

Keywords: multiscale problem, diffusion in random media, stochastic modeling, equation-free


D. Xiu, J.S. Hesthaven. “High Order Collocation Methods for Differential Equations with Random Inputs,” In SIAM Journal on Scientific Computing, Vol. 27, No. 3, pp. 1118--1139. 2005.
DOI: 10.1137/040615201

ABSTRACT

Recently there has been a growing interest in designing efficient methods for the solution of ordinary/partial differential equations with random inputs. To this end, stochastic Galerkin methods appear to be superior to other nonsampling methods and, in many cases, to several sampling methods. However, when the governing equations take complicated forms, numerical implementations of stochastic Galerkin methods can become nontrivial and care is needed to design robust and efficient solvers for the resulting equations. On the other hand, the traditional sampling methods, e.g., Monte Carlo methods, are straightforward to implement, but they do not offer convergence as fast as stochastic Galerkin methods. In this paper, a high-order stochastic collocation approach is proposed. Similar to stochastic Galerkin methods, the collocation methods take advantage of an assumption of smoothness of the solution in random space to achieve fast convergence. However, the numerical implementation of stochastic collocation is trivial, as it requires only repetitive runs of an existing deterministic solver, similar to Monte Carlo methods. The computational cost of the collocation methods depends on the choice of the collocation points, and we present several feasible constructions. One particular choice, basedon sparse grids, depends weakly on the dimensionality of the random space and is more suitable for highly accurate computations of practical applications with large dimensional random inputs. Numerical examples are presented to demonstrate the accuracy and efficiency of the stochastic collocation methods.

Keywords: collocation methods, stochastic inputs, differential equations, uncertainty quantification


D. Xiu, R. Ghanem, I.G. Kevrekidis. “An Equation-free, Multiscale Approach to Uncertainty Quantification,” In IEEE Computing in Science and Engineering Journal (CiSE), Vol. 7, No. 3, pp. 16--23. 2005.
DOI: 10.1109/MCSE.2005.46

ABSTRACT

×
The authors' equation- and Galerkin-free computational approach to uncertainty quantification for dynamical systems conducts UQ computations using short bursts of appropriately initialized ensembles of simulations. Their basic procedure estimates the quantities arising in stochastic Galerkin computations.

Keywords: Analytical models, Computational modeling, Context modeling, Microscopy, Nonlinear equations, Partial differential equations, Performance analysis, Sampling methods, Stochastic processes, Uncertainty


B. Yilmaz, R.S. MacLeod, B.B. Punske, B. Taccardi, D.H. Brooks. “Training Set Selection for Statistical Estimation of Epicardial Activation Mapping from Intravenous Multielectrode Catheters,” In IEEE Trans Biomed. Eng., pp. (in press). 2005.


B. Yilmaz, R.S. MacLeod, B.B. Punske, B. Taccardi, D.H. Brooks. “Venous Catheter Based Mapping of Ectopic Epicardial Activation: Training Data Set Selection for Statistical Estimation,” In IEEE Trans Biomed Eng, Vol. 52, No. 11, pp. 1823--1831. November, 2005.


S.-E. Yoon, P. Lindstrom, V. Pascucci, D. Manocha. “Cache-Oblivious Mesh Layouts,” In ACM Transactions on Graphics: ACM SIGGRAPH 2005 Papers, Vol. 24, No. 3, pp. 886--893. August, 2005.


S.-E. Yoon, P. Lindstrom, V. Pascucci, D. Manocha. “Cache-Oblivious Layouts of Polygonal Meshes,” In Proceedings of Massive 2005 (workshop on Massive Geometric Data Sets), pp. 29--33. 2005.