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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:


h-p Efficiently: Implementing Finite and Spectral/hp Element Methods to Achieve Optimal Performance for Low- and High-Order Discretisations
P.E.J. Vos, S.J. Sherwin, R.M. Kirby. In Journal of Computational Physics, Vol. 229, No. 13, pp. 5161--5181. 2010.



Quantifying Variability in Radiation Dose Due to Respiratory-Induced Tumor Motion
S.E. Geneser, J.D. Hinkle, R.M. Kirby, Brian Wang, B. Salter, S. Joshi. In Medical Image Analysis, Vol. 15, No. 4, pp. 640--649. 2010.
DOI: 10.1016/j.media.2010.07.003



Towards the Development on an h-p-Refinement Strategy Based Upon Error Estimate Sensitivity
P.K. Jimack, R.M. Kirby. In Computers and Fluids, Vol. 46, No. 1, pp. 277--281. 2010.
DOI: 10.1016/j.compfluid.2010.08.003

The use of (a posteriori) error estimates is a fundamental tool in the application of adaptive numerical methods across a range of fluid flow problems. Such estimates are incomplete however, in that they do not necessarily indicate where to refine in order to achieve the most impact on the error, nor what type of refinement (for example h-refinement or p-refinement) will be best. This paper extends preliminary work of the authors (Comm Comp Phys, 2010;7:631–8), which uses adjoint-based sensitivity estimates in order to address these questions, to include application with p-refinement to arbitrary order and the use of practical a posteriori estimates. Results are presented which demonstrate that the proposed approach can guide both the h-refinement and the p-refinement processes, to yield improvements in the adaptive strategy compared to the use of more orthodox criteria.



Quantificiation of Errors Introduced in the Numerical Approximation and Implementation of Smoothness-Increasing Accuracy Conserving (SIAC) Filtering of Discontinuous Galerkin (DG) Fields
H. Mirzaee, J.K. Ryan, R.M. Kirby. In Journal of Scientific Computing, Vol. 45, pp. 447-470. 2010.



Resolution Strategies for the Finite-Element-Based Solution of the ECG Inverse Problem
D.F. Wang, R.M. Kirby, C.R. Johnson. In IEEE Transactions on Biomedical Engineering, Vol. 57, No. 2, pp. 220--237. February, 2010.



Decoupling and Balancing of Space and Time Errors in the Material Point Method (MPM)
M. Steffen, R.M. Kirby, M. Berzins. In International Journal for Numerical Methods in Engineering, Vol. 82, No. 10, pp. 1207--1243. 2010.



Scientific Grand Challenges: Opportunities in biology at the Extreme Scale of Computing
M. Ellisman, R. Stevens, M. Colvin, T. Schlick, E. Delong, G. Olsen, J. George, G. Karniakadis, C.R. Johnson, N. Sematova. Note: DOE Office of Advanced Scientific Computing Research, August, 2009.



Incorporating patient breathing variability into a stochastic model of dose deposition for stereotactic body radiation therapy
S.E. Geneser, R.M. Kirby, Brian Wang, B. Salter, S. Joshi. In Information Processing in Medical Imaging, Lecture Notes in Computer Science LNCS, Vol. 5636, pp. 688--700. 2009.
PubMed ID: 19694304



Finite Element Discretization Strategies for the Inverse Electrocardiographic (ECG) Problem
D.F. Wang, R.M. Kirby, C.R. Johnson. In Proceedings of the 11th World Congress on Medical Physics and Biomedical Engineering, Munich, Germany, Vol. 25/2, pp. 729-732. September, 2009.



Finite Element Refinements for Inverse Electrocardiography: Hybrid-Shaped Elements, High-Order Element Truncation and Variational Gradient Operator
D.F. Wang, R.M. Kirby, C.R. Johnson. In Proceeding of Computers in Cardiology 2009, Park City, September, 2009.



A Framework for Exploring Numerical Solutions of Advection Reaction Diffusion Equations using a GPU Based Approach
A.R. Sanderson, M.D. Meyer, R.M. Kirby, C.R. Johnson. In Journal of Computing and Visualization in Science, Vol. 12, pp. 155--170. 2009.
DOI: 10.1007/s00791-008-0086-0



Subject-specific, multiscale simulation of electrophysiology: a software pipeline for image-based models and application examples
R.S. MacLeod, J.G. Stinstra, S. Lew, R.T. Whitaker, D.J. Swenson, M.J. Cole, J. Krüger, D.H. Brooks, C.R. Johnson. In Philosophical Transactions of The Royal Society A, Mathematical, Physical & Engineering Sciences, Vol. 367, No. 1896, pp. 2293--2310. 2009.



Hexahedral Mesh Generation for Biomedical Models in SCIRun
J.F. Shepherd, C.R. Johnson. In Engineering with Computers, Vol. 25, No. 1, pp. 97--114. 2009.



Comparison of Consistent Integration Versus Adaptive Quadrature for Taming Aliasing Errors
SCI Technical Report, H. Mirzaee, C. Eskilsson, S.J. Sherwin, R.M. Kirby. No. UUSCI-2009-008, SCI Institute, University of Utah, 2009.



The SCIJump Framework for Parallel and Distributed Scientific Computing
S.G. Parker, K. Damevski, A. Khan, A. Swaminathan, C.R. Johnson. In Advanced Computational Infrastructures for Parallel and Distributed Adaptive Applications, Edited by Manish Parashar and Xiaolin Li and Sumir Chandra, Wiley-Blackwell, pp. 149--170. 2009.
DOI: 10.1002/9780470558027.ch9



A Meshing Pipeline for Biomedical Models
M. Callahan, M.J. Cole, J.F. Shepherd, J.G. Stinstra, C.R. Johnson. In Engineering with Computers, Vol. 25, No. 1, SpringerLink, pp. 115-130. 2009.
DOI: 10.1007/s00366-008-0106-1



Formal Verification of Practical MPI Programs
A. Vo, S. Vakkalanka, M. Delisi, G. Gopalakrishnan, R.M. Kirby, R. Thakur. In Proceedings of 14th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP), Raleigh, NC, pp. 261--270. February 14-18, 2009.



Particle-based Sampling and Meshing of Surfaces in Multimaterial Volumes
M.D. Meyer, R.T. Whitaker, R.M. Kirby, C. Ledergerber, H. Pfister. In IEEE Transactions on Visualization and Computer Graphics, Vol. 14, No. 6, pp. 1539--1546. 2008.



Hexahedral Mesh Generation Constraints
J.F. Shepherd, C.R. Johnson. In Journal of Engineering with Computers, Vol. 24, No. 3, pp. 195--213. 2008.



Filtering in Legendre Spectral Methods
J.S. Hesthaven, R.M. Kirby. In Mathematics of Computation, Vol. 77, No. 263, pp. 1425--1452. 2008.