## Martin BerzinsParallel ComputingGPUs |
## Mike KirbyFinite Element MethodsUncertainty Quantification GPUs |
## Valerio PascucciScientific Data Management |
## Chris JohnsonProblem Solving Environments |

## Ross WhitakerGPUs |
## Chuck HansenGPUs |

Quantifying Variability in Radiation Dose Due to Respiratory-Induced Tumor MotionS.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 SensitivityP.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. |

Incorporating patient breathing variability into a stochastic model of dose deposition for stereotactic body radiation therapyS.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 |

A Framework for Exploring Numerical Solutions of Advection Reaction Diffusion Equations using a GPU Based ApproachA.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 |

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

The SCIJump Framework for Parallel and Distributed Scientific ComputingS.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 ModelsM. 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 |

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