SCI Publications
2008
J. Li, D. Xiu.
“On Numerical Properties of the Ensemble Kalman Filter for Data Assimilation,” In Computer Methods in Applied Mechanics and Engineering, Vol. 197, No. 43--44, pp. 3574--3583. 2008.
DOI: 10.1016/j.cma.2008.03.022
Ensemble Kalman filter (EnKF) has been widely used as a sequential data assimilation method, primarily due to its ease of implementation resulting from replacing the covariance evolution in the traditional Kalman filter (KF) by an approximate Monte Carlo ensemble sampling. In this paper rigorous analysis on the numerical errors of the EnKF is conducted in a general setting. Error bounds are provided and convergence of the EnKF to the exact Kalman filter is established. The analysis reveals that the ensemble errors induced by the Monte Carlo sampling can be dominant, compared to other errors such as the numerical integration error of the underlying model equations. Methods to reduce sampling errors are discussed. In particular, we present a deterministic sampling strategy based on cubature rules (qEnKF) which offers much improved accuracy. The analysis also suggests a less obvious fact — more frequent data assimilation may lead to larger numerical errors of the EnKF. Numerical examples are provided to verify the theoretical findings and to demonstrate the improved performance of the qEnKF.
Keywords: Data assimilation, Uncertainty quantification, Ensemble Kalman filter
L. Lins, D. Koop, E.W. Anderson, S.P. Callahan, E. Santos, C.E. Scheidegger, J. Freire, C.T. Silva.
“Examining Statistics of Workflow Evolution Provenance: A First Study,” In Proceedings of the 20th International Conference on Scientific and Statistical Database Management (SSDBM), pp. 573--579. 2008.
DOI: 10.1007/978-3-540-69497-7_40
W. Lin, Q. Zhu, W. Gao, Y. Chen, C.-H. Toh, M. Styner, G. Gerig, J.K. Smith, B. Biswal, J. Gilmore.
“Functional Connectivity Magnetic Resonance Imaging Reveals Cortical Functional Connectivity in the Developing Brain,” In American Journal of Neuroradiology, Vol. 29, pp. 1883--1889. Fall, 2008.
Y. Livnat, S.G. Parker, C.R. Johnson.
“Fast Isosurface Extraction Methods for Large Image Data Sets,” In Handbook of Medical Image Processing and Analysis, 2nd edition, Ch. 47, Note: (to appear), Edited by Isaac N. Bankman, Elsevier, pp. 801--816. 2008.
M. Lizier, J.F. Shepherd, L.G. Nonato, J. Comba, C.T. Silva.
“Comparing Techniques for Tetrahedral Mesh Generation,” In Proceedings of the Inaugural International Conference of the Engineering Mechanics Institute (EM 2008), pp. (accepted). 2008.
J. Luitjens, Q. Meng, M. Berzins, T. Henderson.
“Improving the Load Balance of Parallel Adaptive Mesh Refined Simulations,” SCI Technical Report, No. UUSCI-2008-007, University of Utah School of Computing, 2008.
J. Luitjens, B. Worthen, M. Berzins, T. Henderson.
“Scalable Parallel AMR for the Uintah Multiphysics Code,” In Petascale Computing Algorithms and Applications, Ch. 4, CRC Press LLC., pp. 67--82. 2008.
T.J. Lujan, C.J. Underwood, N.T. Jacobs, J.A. Weiss.
“Contribution of Glycosaminoglycans to Viscoelastic Tensile Behavior of Human Ligament,” In Journal of Applied Physiology, Vol. 106, No. 2, pp. 423--431. 2008.
T. Martin, E. Cohen, R.M. Kirby.
“Volumetric Parameterization and Trivariate B-spline Fitting using Harmonic Functions,” In Proceedings of ACM Solid and Physical Modeling, Stony Brook, NY, Note: Awarded Best Paper, pp. 269-280. 2008.
C.J. McGann, E.G. Kholmovski, R.S. Oakes, J.J. Blauer, M. Daccarett, N. Segerson, K.J. Airey, N. Akoum, E. Fish, T.J. Badger, E.V. DiBella, D.L. Parker, R.S. MacLeod, N.F. Marrouche.
“New Magnetic Resonance Imaging-Based Method for Defining the Extent of Left Atrial Wall Injury After the Ablation of Atrial Fibrillation,” In Journal of the American College of Cardiology, Vol. 52, No. 15, pp. 1263--1271. Oct 7, 2008.
Q. Meng, J. Luitjens, M. Berzins.
“A Comparison of Load Balancing Algorithms for AMR in Uintah,” SCI Technical Report, No. UUSCI-2008-006, University of Utah, 2008.
S. Mergen, C. Heuser, J. Freire.
“Querying Structured Information Sources on the Web,” In Proceedings of the 10th International Conference on Information Integration and Web-based Applications and Services, pp. 470--476. 2008.
D. Merck, G. Tracton, R. Saboo, J. Levy, E. Chaney, S. Pizer, S. Joshi.
“Training models of anatomic shape variability,” In Medical Physics, Vol. 35, No. 8, pp. 3584--3596. 2008.
PubMed ID: 18777919
M.D. Meyer, R.T. Whitaker, R.M. Kirby, C. Ledergerber, H. Pfister.
“Particle-based Sampling and Meshing of Surfaces in Multimaterial Volumes,” In IEEE Transactions on Visualization and Computer Graphics, Vol. 14, No. 6, pp. 1539--1546. 2008.
M.D. Meyer.
“Dynamic Particle Systems for Adaptive Sampling of Implicit Surfaces,” School of Computing, University of Utah, 2008.
A ubiquitous requirement in many mathematical and computational problems is a set of well-placed point samples. For producing very even distributions of samples across complex surfaces, a dynamic particle system is a controllable mechanism that naturally accommodates strict sampling requirements. The systemfirst constrains particles to a surface, and then moves the particles across the surface until they are arranged in minimal energy configurations. Adaptivity is added into the system by scaling the distance between particles, causing higher densities of points around surface features. In this dissertation we explore and refine the dynamics of particle systems for generating efficient and adaptive point samples of implicit surfaces.
Throughout this dissertation, we apply the adaptive particle system framework to several application areas. First, efficient visualizations of high-order finite element datasets are generated by developing adaptivity metrics of surfaces that exist in the presence of curvilinear coordinate transformation. Second, a framework is proposed that meets fundamental sampling constraints of Delaunay-based surface reconstruction algorithms. In meeting these constraints, the particle distributions produce nearly-regular, efficient isosurface tessellation that are geometrically and topologically accurate. And third, a novel analytic representation of material boundaries in multimaterial volume datasets is developed, as well as a set of projection operators, that allow for explicit sampling of nonmanifold material intersections. Using a tetrahedral labeling algorithm, the material intersections are extracted as watertight, nonmanifold meshes that are well-suited for simulations.
L. Moreau, B. Ludäscher, I. Altintas, R. Barga, S. Bowers, S.P. Callahan, G. Chin Jr., B. Clifford, S. Cohen, S. Cohen-Boulakia, S. Davidson, E. Deelman, L. Digiampietri, I. Foster, J. Freire, J. Frew, J. Futrelle, T. Gibson, Y. Gil, C. Goble, J. Golbeck, P. Groth, D. A. Holland, S. Jiang, J. Kim, D. Koop, A. Krenek, T. McPhillips, G. Mehta, S. Miles, D. Metzger, S. Munroe, J. Myers, B. Plale, N. Podhorszki, V. Ratnakar, E. Santos, C.E. Scheidegger, K. Schuchardt, M. Seltzer, Y. L. Simmhan, C.T. Silva, P. Slaughter, E. Stephan, R. Stevens, D. Turi, H.T. Vo, M. Wilde, J. Zhao, Y. Zhao.
“The First Provenance Challenge,” In Concurrency and Computation: Practice and Experience, Vol. 20, No. 5, pp. 409--418. April, 2008.
L. Moreau, J. Freire, J. Futrelle, R.E. McGrath, J. Myers, P. Paulson.
“The Open Provenance Model: An Overview,” In Provenance and Annotation of Data and Processes, Lecture Notes in Computer Science (LNCS), pp. 323--326. 2008.
ISBN: 978-3-540-89964-8
N. Mukherjee, C. Kang, H.M. Wolfe, B.S. Hertzberg, J.K. Smith, W. Lin, G. Gerig, R.M. Hamer, J.H. Gilmore.
“Discordance of Prenatal and Neonatal Brain Development in Twins,” In Early Human Development, pp. (in press). August, 2008.
E.P. Newren, A.L. Fogelson, R.D. Guy, R.M. Kirby.
“A Comparison of Implicit Solvers for the Immersed Boundary Equations,” In Computer Methods in Applied Mechanics and Engineering, Vol. 197, No. 25--28, pp. 2290--2304. 2008.
DOI: 10.1016/j.cma.2007.11.030
H. Nguyen, T. Nguyen, J. Freire.
“Learning to Extract Form Labels,” In Proceedings of the VLDB Endowment, Vol. 1, No. 1, VLDB Endowment, pp. 684--694. Aug, 2008.
DOI: 10.14778/1453856.1453931