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Journal Articles

  1. N. Alger, V. Rao, A. Myers, T. Bui-Thanh, and O. Ghattas, Scalable Matrix-Free Adaptive Product-Convolution Approximation for Locally Translation-Invariant Operators, SIAM Journal on Scientific Computing, submitted.
  2. B. Crestel, G. Stadler, and O. Ghattas, A comparative study of joint regularizations for joint inverse problems, Inverse Problems, submitted.
  3. Harriet Li, Vikram Garg, Karen Willcox, Model Adaptivity for Goal-Oriented Inference using Adjoints, Computer Methods in Applied Mechanics and Engineering, Vol. 331, 1--22, 2018.
  4. Lima, E.A.B.F.; Ozkan, A.; Ghousifam, N.; Oden, J.T.; Shahmoradi, A.; Rylander, M.N.; Wohlmuth, B.; and Yankeelov, T.E., Calibration of Multi-Parameter Models of Avascular Tumor Growth Using Time Resolved Microscopy Data, Journal of Computational Biology, in review, 2018.
  5. Morrison, Rebecca E., Todd A. Oliver, and Robert D. Moser, Representing model inadequacy: A stochastic operator approach, SIAM/ASA Journal on Uncertainty Quantification, 6/2/457-496, 2018.
  6. J. T. Oden, Adaptive multiscale predictive modeling, Acta Numerica, 27//353-450, 2018.
  7. Matthew Parno and Youssef Marzouk, Transport map accelerated Markov chain Monte Carlo, SIAM/ASA Journal on Uncertainty Quantification, 6(2), 645-682, 2018.
  8. Peherstorfer, B. and Willcox, K. and Gunzburger, M., Survey of multifidelity methods in uncertainty propagation, inference, and optimization, SIAM Review, to appear, 2018.
  9. Qian, E., Peherstorfer, B., O'Malley, D., Vesselinov, V., and Willcox, K., Multifidelity Monte Carlo estimation of variance and sensitivity indices, SIAM/ASA Journal on Uncertainty Quantification, 6 (2): 683-706, 2018.
  10. Rocha, H.L.; Almeida, R.C.; Lima, E.A.B.F.; Resende, A.C.M.; Oden, J.T.; and Yankeelov, T.E., A Hybrid Three-Scale Model of Tumor Growth, Mathematical Models and Methods in Applied Science, 28/1/61-93, 2018.
  11. K. Wang, T. Bui-Thanh, and O. Ghattas, A randomized maximum a posteriori method for posterior sampling of high dimensional nonlinear Bayesian inverse problems, SIAM Journal on Scientific Computing, 40(1), A142-A171, 2018.
  12. Ralf Zimmermann, Benjamin Peherstorfer, and Karen Willcox, Geometric Subspace Updates with Applications to Online Adaptive Nonlinear Model Reduction, SIAM Journal on Matrix Analysis and Applications, 39(1), 234–261, 2018.
  13. Alen Alexanderian, Noemi Petra, Georg Stadler, and Omar Ghattas, Mean-variance risk-averse optimal control of systems governed by PDEs with random parameter fields using quadratic approximations, SIAM/ASA Journal on Uncertainty Quantification, 5(1), 1166-1192, 2017.
  14. Nicholas Alger, Umberto Villa, Tan Bui-Thanh, and Omar Ghattas, A data scalable augmented Lagrangian KKT preconditioner for large scale inverse problems, SIAM Journal on Scientific Computing, 39(5), A2365-A2393., 2017.
  15. T. Butler, L. Graham, S. Mattis, and S. Walsh, A measure-theoretic interpretation of sample based numerical integration with applicaitons to inverse and prediction problems under uncertainty, SIAM Journal on Scientific Computing, 39 (5): A2072-A2098, 2017.
  16. J. H. Chaudhry, D. Estep, M. Gunzburger, Exploration of Efficient Reduced-Order Modeling and A Posteriori Error Estimation, International Journal of Numerical Methods for Engineering, 111(2), 103-122, 2017.
  17. P. Chen, U. Villa, and O. Ghattas, Hessian-based adaptive sparse quadrature for infinite-dimensional Bayesian inverse problems, Computer Methods in Applied Mechanics and Engineering, 327:147-172, 2017.
  18. Benjamin Crestel, Alen Alexanderian, Georg Stadler, and Omar Ghattas, A-optimal encoding weights for nonlinear inverse problems, with application to the Helmholtz inverse problem, Inverse problems, 33(7), 074008, 2017.
  19. A. Damle, L. Lin, and L. Ying, Accelerating selected columns of the density matrix computations via approximate column selection, SIAM J. Sci. Comput. , 39, 1178, 2017.
  20. Farrell-Maupin, K. and Oden, J.T., Adaptive Selection and Validation of Models of Complex Systems in the Presence of Uncertainty, Research in Mathematical Sciences, 4/1/14, 2017.
  21. P. K. Kang, M. Dentz, T. Le Borgne, S. Lee, and R. Juanes, Anomalous transport in disordered fracture networks: Spatial Markov model for dispersion with variable injection modes , Advances in Water Resources, Vol. 106, pp. 80-94, 2017.
  22. P. K. Kang, J. Lee, X. Fu, S. Lee, P. K. Kitanidis, and R. Juanes, Improved characterization of heterogeneous permeability in saline aquifers from transient pressure data during freshwater injection, Water Resources Research, 2017.
  23. Kramer, B., Marques, A., Peherstorfer, B., Villa, U. and Willcox, K., Multifidelity probability estimation via fusion of estimators, ACDL TR-2017-3, Submitted, 2017.
  24. Kramer, B., Peherstorfer, B. and Willcox, K., Feedback Control for Systems with Uncertain Parameters Using Online-Adaptive Reduced Models, SIAM Journal on Applied Dynamical Systems, Vol. 16, No. 3, pp. 1563-1586, 2017.
  25. Li, Y., Ying, L., Distributed-memory hierarchical interpolative factorization, Research in the Mathematical Sciences, 4(1), 12, 2017.
  26. Lima, E.A.B.F.; Oden, J.T.; Wohlmuth, B.; Shahmoradi, A.; Hormuth, D.A.; Yankeelov, T.E.; Scarabosio, L.; and Horger, T., Selection and Validation of Predictive Models on Tumor Growth based on Noninvasive Imaging Data, Computer Methods in Applied Mechanics and Engineering, 327//277-305, 2017.
  27. Lin, L., Xu, Z., Ying, L., Adaptively compressed polarizability operator for accelerating large scale ab initio phonon calculations, SIAM Journal on Multiscale Modeling & Simulation, 15(1), 29-55, 2017.
  28. Liu, F., Ying, L. , Localized sparsifying preconditioner for periodic indefinite systems, Communications in Mathematical Sciences, 15(4), 1155-1169, 2017.
  29. Minden, V., Damle, A., Ho, K. L., Ying, L. , Fast spatial Gaussian process maximum likelihood estimation via skeletonization factorizations, SIAM Journal of Multiscale Modeling & Simulation, 15(4), 1584-1611, 2017.
  30. Rahman, M.M.; Feng, Yusheng; Yankeelov, Thomas E.; and Oden, J. Tinsley., A Fully Coupled Space-Time Multiscale Modeling Framework for Predicting Tumor Growth, Computer Methods in Applied Mechanics and Engineering, 320//261-286, 2017.
  31. Johann Rudi, Georg Stadler, and Omar Ghattas, Weighted BFBT preconditioner for Stokes flow problems with highly heterogeneous viscosity. , SIAM Journal on Scientific Computing, 39(5), S272-S297., 2017.
  32. A. Spantini and T. Cui and K. E. Willcox and L. Tenorio and Y. M. Marzouk, Goal-oriented optimal approximations of Bayesian linear inverse problems, SIAM Journal on Scientific Computing, 9(5), S167-S196., 2017.
  33. Lexing Ying, Tensor Network Skeletonization, SIAM Journal on Multiscale Modeling and Simulation, 15 (4): 1423-1447, 2017.
  34. S. Yoon, J. R. Williams, R. Juanes, and P. K. Kang, Maximizing the value of pressure data in saline aquifer characterization, Advances in Water Resources, 109, 14-28, 2017.
  35. P. de Anna, B. Quaife, G. Biros, and R. Juanes, Prediction of velocity distribution from pore structure in simple porous media, Physical Review Fluids, 2(12), 124103, 2017.
  36. Alen Alexanderian, Philip J. Gloor, and Omar Ghattas, On Bayesian A-and D-optimal experimental designs in infinite dimensions, Bayesian Analysis, 11(3), 671-695, 2016.
  37. Alen Alexanderian, Noemi Petra, Georg Stadler, and Omar Ghattas, A fast and scalable method for A-optimal design of experiments for infinite-dimensional Bayesian nonlinear inverse problems. , SIAM Journal on Scientific Computing, 38(1):A243–A272, 2016.
  38. J. H. Chaudhry, D. Estep, S. Tavener, V. Carey, and J . Sandelin, A posteriori error analysis of two stage computation methods with application to efficient resource allocation and the Parareal Algorithm, SIAM Journal on Numerical Analysis, 54(5), 2974-3002., 2016.
  39. Cueto-Felgueroso, Luis and Juanes, Ruben, A discrete-domain description of multiphase flow in porous media: Rugged energy landscapes and the origin of hysteresis, Geophysical Research Letters, 43, 2016.
  40. T. Cui and K. J. H. Law and Y. M. Marzouk, Dimension-independent likelihood-informed MCMC, Journal of Computational Physics, 304, 109-137, 2016.
  41. T. Cui and Y. M. Marzouk and K. E. Willcox, Scalable posterior approximations for large-scale Bayesian inverse problems via likelihood-informed parameter and state reduction, Journal of Computational Physics, 315, 363-387, 2016.
  42. X. Fu, L. Cueto-Felgueroso, and R. Juanes, Thermodynamic coarsening arrested by viscous fingering in partially-miscible binary mixtures, Physical Review E, 94, 033111, 2016.
  43. Vikram V. Garg and Luis Tenorio and Karen Willcox, Minimum Local Distance Density Estimation, Communications in Statistics - Theory and Methods, 46(1), 148-164, 2016.
  44. Amir Gholami and George Biros, AccFFT: A library for distributed-memory FFT on CPU and GPU architectures, SIAM Journal on Scientific Computing, 38(3):C280–C306, 2016.
  45. A. Gholami, D. Malhotra, H. Sundar, and G. Biros, FFT, FMM, or multigrid? A comparative study of state-of-the-art Poisson solvers, SIAM Journal on Scientific Computing, 38 (3), pp. 280–306, 2016.
  46. Grasinger, M., O'Malley, D., Vesselinov, V.V., Karra, S., Decision Analysis for Robust CO2 Injection: Application of Bayesian-Information-Gap Decision Theory, International Journal of Greenhouse Gas Control, 49:73-80, 2016.
  47. P. K. Kang, S. Brown, and R. Juanes, Emergence of anomalous transport in stressed rough fractures., Earth and Planetary Science Letters, 454:46-54, 2016.
  48. P. K. Kang, Y. Zheng, X. Fang, R. Wojcik, D. McLaughlin, S. Brown, M. C. Fehler, D. R. Burns, and R. Juanes, Sequential approach to joint flow–seismic inversion for improved characterization of fractured media, Water Resources Research, 52(2), 903-919, 2016.
  49. Lima, E.A.B.F.; Oden, J.T.; Hormuth, D.A.; Yankeelov, T.E.; and Almeida, R.C., Selection, Calibration, and Validation of Models of Tumor Growth, Mathematical Models and Methods in Applied Sciences, 26/12/2341-2368, 2016.
  50. Lin, Y., O'Malley, D., Vesselinov, V.V., A computationally efficient parallel Levenberg-Marquardt algorithm for highly parameterized inverse model analyses, Water Resources Research, 52(9): 6948-6977, 2016.
  51. Liu, F., Ying, L., Additive sweeping preconditioner for the Helmholtz equation, SIAM Journal on Multiscale Modeling & Simulation, 14(2), 799-822, 2016.
  52. Liu, F., Ying, L., Recursive sweeping preconditioner for the three-dimensional Helmholtz equation, SIAM Journal on Scientific Computing, 38(2), A814-A832, 2016.
  53. Lu, J., Ying, L., Fast algorithm for periodic density fitting for Bloch waves, Annals of Mathematical Sciences and Applications,, 1(2), 321-339, 2016.
  54. Lu, J., Ying, L., Sparsifying preconditioner for soliton calculations, Journal of Computational Physics, 315, 458-466, 2016.
  55. D. Malhotra and G. Biros, Algorithm 967: A distributed memory fast multipole method for volume potentials, ACM Transactions on Mathematical Software, 43 (2), pp. 1–27, 2016.
  56. Mang, A., Biros, G., Constrained H1-regularization schemes for diffeomorphic image registration, SIAM journal on imaging sciences, 9(3), 1154-1194, 2016.
  57. Minden, V., Damle, A., Ho, K. L., Ying, L. , A technique for updating hierarchical skeletonization-based factorizations of integral operators, SIAM Journal of Multiscale Modeling & Simulation, 14(1), 42-64, 2016.
  58. Nicolaides, Christos and Juanes, Ruben and Cueto-Felgueroso, Luis, Self-organization of network dynamics into local quantized states, Sci Rep, (6), 21360., 2016.
  59. M. Opgenoord and D. Allaire and K. Willcox, Variance-Based Sensitivity Analysis to Support Simulation-based Design under Uncertainty, Journal of Mechanical Design, 138(11), 111410, 2016.
  60. M. Parno and T. Moselhy and Y.M. Marzouk, A multiscale strategy for Bayesian inference using transport maps, SIAM/ASA Journal on Uncertainty Quantification, 4(1), 1160-1190, 2016.
  61. Benjamin Peherstorfer and Karen Willcox, Dynamic data-driven model reduction: adapting reduced models from incomplete data, Advanced Modeling and Simulation in Engineering Sciences , 3:11, 2016.
  62. B. Peherstorfer and T. Cui and Y. M. Marzouk and K. E. Willcox, Multifidelity importance sampling, Computer Methods in Applied Mechanics and Engineering, 300, 490-509, 2016.
  63. Peherstorfer, B. and Willcox, K., Data-driven operator inference for nonintrusive projection-based model reduction, Computer Methods in Applied Mechanics and Engineering, 306, 196-215, 2016.
  64. Peherstorfer, B. and Willcox, K. and Gunzburger, M., Optimal model management for multifidelity Monte Carlo estimation, SIAM Journal on Scientific Computing, 38(5), A3163-A3194, 2016.
  65. M. Presho, J. Galvis, A mass conservative generalized multiscale finite element method applied to two-phase flow in heterogeneous porous media, Journal of Computational and Applied Mathematics, 296, 376-388, 2016.
  66. E. Qian and M. Grepl and K. Veroy and K. Willcox, A certified trust region reduced basis approach to PDE-constrained optimization, SIAM Journal on Scientific Computing, 39(5), S434-S460., 2016.
  67. B. Quaife and G. Biros, Adaptive Time Stepping for Vesicle Suspensions, Journal on Computational Physics, pp. 478–499, 306 (1), 2016.
  68. Samii, A., Michoski, C., Dawson, C., A parallel and adaptive hybridized discontinuous Galerkin method for anisotropic nonhomogeneous diffusion, Computer Methods in Applied Mechanics and Engineering, 304, 118-139, 2016.
  69. A. Solonen and T. Cui and J. Hakkarainen and Y. M. Marzouk, On dimension reduction in Gaussian filters, Inverse Problems, 2(4):045003, 2016.
  70. Zhang, X., Vesselinov, V.V., Energy-Water Nexus: Balancing the Tradeoffs between Two-Level Decision Makers, Applied Energy, 183, 77-87, 2016.
  71. H. Zhu, S. Li, S. Fomel, G. Stadler, and O. Ghattas, A Bayesian approach to estimate uncertainty for full waveform inversion using a priori information from depth migration, Geophysics, 81(5):R307-R323, 2016.
  72. Hongyu Zhu, Noemi Petra, Georg Stadler, Tobin Isaac, Thomas J. R. Hughes, and Omar Ghattas, Inversion of geothermal heat flux in a thermomechanically coupled nonlinear Stokes ice sheet model., The Cryosphere, 10:1477–1494, 2016.
  73. R. Zimmermann and K. Willcox, An Accelerated Greedy Missing Point Estimation Procedure, SIAM Journal on Scientific Computing, 38(5), A2827-A2850., 2016.
  74. T. Arbogast, D. Estep, B. Sheehan, and S. Tavener, A posteriori error estimates for mixed finite element and finite volume methods for parabolic problems coupled through a boundary with non-matching discretizations, SIAM ASA Journal on Uncertainty Quantification, 3, 169-198, 2015.
  75. Barajas-Solano, D. A., Wohlberg, B., Vesselinov, V.V., Tartakovsky, D. M., Linear Functional Minimization for Inverse Modeling, Water Resources Research, 51:4516–4531, 2015.
  76. Tan Bui-Thanh and Omar Ghattas, A scalable MAP solver for Bayesian inverse problems with Besov priors. , Inverse Problems and Imaging, 9(1):27–54, 2015.
  77. T. Butler, L. Graham, D. Estep, C. Dawson, and J.J. Westerink, Definition and solution of a stochastic inverse problem for the Manning's n parameter field in hydrodynamic models, Advances in Water Resources, 78, 60–79, 2015.
  78. T. Butler, A. Huhtala, and M. Juntunen, Quantifying uncertainty in material damage from vibrational data, Journal of Computational Physics, 283:414 – 435, 2015.
  79. Troy Butler, Clint Dawson, Donald Estep, Steven Mattis, Velimir Vesselinov, Parameter estimation and prediction for groundwater contamination based on measure theory, Water Resources Research, 52, 7808-7629, 2015.
  80. J. H. Chaudry, D. Estep, V. Ginting, and S. Tavener, A posteriori analysis for iterative solvers for non-autonomous evolution problems, SIAM ASA Journal on Uncertainty Quantification, 3, 2015.
  81. T. Cui and Y. M. Marzouk and K. E. Willcox, Data-driven model reduction for the Bayesian solution of inverse problems, International Journal for Numerical Methods in Engineering, 102, 966-990, 2015.
  82. Farrell, K.; Oden, J.T.; and Faghihi, D., A Bayesian framework for adaptive selection, calibration, and validation of coarse-grained models of atomistic systems, Journal of Computational Physics, vol. 295, pp 189-208, 2015.
  83. Tobin Isaac, Noemi Petra, Georg Stadler, and Omar Ghattas, Scalable and efficient algorithms for the propagation of uncertainty from data through inference to prediction for large-scale problems, with application to flow of the Antarctic ice sheet. , Journal of Computational Physics, 296:348–368, 2015.
  84. Tobin Isaac, Georg Stadler, and Omar Ghattas, Solution of nonlinear Stokes equations discretized by high-order finite elements on nonconforming and anisotropic meshes, with application to ice sheet dynamics., SIAM Journal on Scientific Computing, 37(6):B804–B833, 2015.
  85. A. Johansson, J. H. Chaudhry, V. Carey, D. Estep, V. Ginting, M. Larson, and S. Tavener, Adaptive finite element solution of multiscale PDE-ODE systems, Computer Methods in Applied Mechanics and Engineering, 287, 150–171, 2015.
  86. Kang, Peter K. and Dentz, Marco and Le Borgne, Tanguy and Juanes, Ruben, Anomalous transport on regular fracture networks: Impact of conductivity heterogeneity and mixing at fracture intersections, Phys. Rev. E, 92, 2015.
  87. Kang, Peter K. and Le Borgne, Tanguy and Dentz, Marco and Bour, Olivier and Juanes, Ruben, Impact of velocity correlation and distribution on transport in fractured media: Field evidence and theoretical model, Water Resources Research, 51, 2015.
  88. Lima, E.A.B.F.; Almeida, Regina C.; and Oden, J.T., Analysis and Numerical Solution of Stochastic Phase-Field Models of Tumor Growth, International Journal for Numerical Methods in Partial Differential Equations, 31 (2), 552-574, 2015.
  89. Lu, Z., Vesselinov, V.V.,, Analytical Sensitivity Analysis of Transient Groundwater Flow in a Bounded Model Domain using Adjoint Method, Water Resources Research, 51(7):5060-5080, 2015.
  90. D. Malhotra and G. Biros, PvFMM: A parallel kernel-independent FMM for particle and volume potentials, Communications in Computational Physics, 18 (3), pp. 808– 830, 2015.
  91. A. Mang and G. Biros, An inexact Newton-Krylov algorithm for constrained diffeomorphic image registration, SIAM Journal on Imaging Sciences, 8 (2), pp. 1030–1069, 2015.
  92. W. B. March, B. Xiao, and G. Biros, ASKIT: Approximate Skeletonization Kernel- Independent Treecode in High Dimensions, SIAM Journal on Scientific Computing, 37 (2), pp. A1089–A1110, 2015.
  93. Nicolaides, Christos and Jha, Birendra and Cueto-Felgueroso, Luis and Juanes, Ruben, Impact of viscous fingering and permeability heterogeneity on fluid mixing in porous media, Water Resources Research, 51, 2015.
  94. Oden, J.T.; Farrell, K.; and Faghihi, D., Estimation of error in observables of coarse-grained models of atomic systems, Advanced Modeling and Simulation in Engineering Sciences, 2:5, 2015.
  95. Oden, J.T.; Lima, E.; Almeida, R.; Feng, Y.S.; Rylander, M.N.; and Fuentes, D. , Toward Predictive Multiscale Modeling of Vascular Tumor Growth, Archives of Computational Methods in Engineering, 23/4/735-779, 2015.
  96. O’Malley, D., Vesselinov, V.V., Bayesian-Information-Gap decision theory with an application to CO2 sequestration, Water Resources Research, 51(9):7080-7089, 2015.
  97. Alizadeh Pahlavan, Amir and Cueto-Felgueroso, Luis and McKinley, Gareth H. and Juanes, Ruben, Thin Films in Partial Wetting: Internal Selection of Contact-Line Dynamics, Phys. Rev. Lett., 115, 2015.
  98. A. Rahimian S.K. Veerapaneni and D. Zorin and G. Biros, Boundary integral method for the flow of vesicles with viscosity contrast in three dimensions, Journal of Computational Physics, pp. 766–786, 2015.
  99. A. Spantini and A. Solonen and T. Cui and J. Martin and L. Tenorio and Y. M. Marzouk, Optimal low-rank approximation of linear {B}ayesian inverse problems, SIAM Journal on Scientific Computing, 37:A2451-A2487, 2015.
  100. Lucas C. Wilcox, Georg Stadler, Tan Bui-Thanh, and Omar Ghattas, Discretely exact derivatives for hyperbolic PDE-constrained optimization problems discretized by the discontinuous Galerkin method. , Journal of Scientific Computing, 63(1):138–162, 2015.
  101. Fu,X. and Cueto-Felgueroso,L. and Bolster,D. and Juanes,R., Rock dissolution patterns and geochemical shutdown of CO2--brine--carbonate reactions during convective mixing in porous media, Journal of Fluid Mechanics, 726:296-315 , 2015.
  102. Alen Alexanderian, Noemi Petra, Georg Stadler, and Omar Ghattas, A-optimal design of experiments for infinite-dimensional Bayesian linear inverse problems with regularized ℓ0-sparsification. , SIAM Journal on Scientific Computing, 36(5):A2122–A2148, 2014.
  103. S. Allu, B. Velamur Asokan, W.A. Shelton, B. Philip, S. Pannala, A generalized multi-dimensional mathematical model for charging and discharging processes in a supercapacitor, Journal of Power Sources, Vol. 256, 369–382, 2014.
  104. Tan Bui-Thanh and Omar Ghattas, An analysis of infinite dimensional Bayesian inverse shape acoustic scattering and its numerical approximation. , SIAM Journal of Uncertainty Quantification, 2(1):203–222, 2014.
  105. Tan Bui-Thanh and Omar Ghattas, A PDE-constrained optimization approach to the discontinuous Petrov-Galerkin method with a trust region inexact Newton-CG solver. , Computer Methods in Applied Mechanics and Engineering, 278:20–40, 2014.
  106. T. Butler, D. Estep, S. Tavener, C. Dawson, J. Westerink, A measure-theoretic computational method for inverse sensitivity problems III: Multiple quantities of interest, SIAM ASA Journal on Uncertainty Quantification, 2, 174-202, 2014.
  107. J. Chaudry, D. Estep, V. Ginting, J. Shadid, and S. Tavener, A posteriori error analysis of IMEX time integration schemes for advection-diffusion-reaction equations, Computer Methods in Applied Mechanics and Engineering, 285, 730-751, 2014.
  108. J. Collins, D. Estep and S. Tavener, A posteriori error analysis for finite element methods with projection operators as applied to explicit time integration techniques, BIT Numerical Mathematics, DOI 10.1007/s10543-014-0534-9, 2014.
  109. J. B. Collins, D. Estep, and S. Tavener, A posteriori error estimation for the Lax-Wendroff finite difference scheme, Journal of Computational and Applied Mathematics, 263C, 299-311, 2014.
  110. T. Cui and J. Martin and Y. M. Marzouk and A. Solonen and A. Spantini, Likelihood-informed dimension reduction for nonlinear inverse problems, Inverse Problems, 30,114015, 2014.
  111. D. Elfverson, D. Estep, F. Hellman, A. Malqvist, Uncertainty quantification for approximate p-quantiles for physical models with stochastic inputs, SIAM ASA Journal on Uncertainty Quantification, 2, 826–850, 2014.
  112. Farrell, Kathryn and Oden, J.T., Calibration and Validation of Coarse-grained Models of Atomic Systems: Application to Semiconductor Manufacturing, Journal of Computational Mechanics, v. 54, pp. 3-19, 2014.
  113. X. Jiang, J. Huang, H. Zhao, B.G. Sumpter, R. Qiao, Dynamics of electrical double layer formation in room-temperature ionic liquids under constant-current charging conditions, J. Physics: Condensed Matter, 26(28), 284109, 2014.
  114. Kang, Peter K. and de Anna, Pietro and Nunes, Joao P. and Bijeljic, Branko and Blunt, Martin J. and Juanes, Ruben, Pore-scale intermittent velocity structure underpinning anomalous transport through 3-D porous media, Geophysical Research Letters, 41, 2014.
  115. Lima, E.A.B.F.; Oden, J.T.; and Almeida, Regina C., A Hybrid Ten-Species Phase-field Mode of Tumor Growth, Mathematical Models and Methods in Applied Science (M3AS), v. 24, no. 13, pp. 2569-2600, 2014.
  116. Oden, J.T., Predictive Computational Science, IACM Expressions, Bulletin for the International Association for Computational Mechanics, no. 35, pp. 2-4, 2014.
  117. O’Malley, D., Vesselinov, V.V., A combined probabilistic/non-probabilistic decision analysis for contaminant remediation, SIAM/ASA J. Uncertainty Quantification, 2(1):607-621, 2014.
  118. Noemi Petra, James Martin, Georg Stadler, and Omar Ghattas, A computational framework for infinite-dimensional Bayesian inverse problems: Part II. Stochastic Newton MCMC with application to ice sheet inverse problems. , SIAM Journal on Scientific Computing, 36(4):A1525– A1555, 2014.
  119. J. Poulson, L. Demanet, N. Maxwell, and L. Ying, A parallel butterfly algorithm, SIAM Journal on Scientific Computing, 36(1), C49–C65, 2014.
  120. Prudencio, E.E., Bauman, P. T.; Faghihi, D.; Ravi-Chandar, K.; and Oden, J.T., A Computational Framework for Dynamic Data Driven Material Damage Control, Based on Bayesian Inference and Model Selection, International Journal for Numerical Methods in Engineering (Special Issue: Tribute to Prof. Ted Belytschko), 102 (3-4), 379-403, 2014.
  121. Prudencio, E.E., Bauman, P. T.; Williams, Stephen V.; Faghihi, D.; Ravi-Chandar, K.; and Oden, J.T., Real-Time Inference of Stochastic Damage in Composite Materials, Journal of Composites B, vol. 67, pp. 209–219, 2014.
  122. B. Quaife and G. Biros, On preconditioners for the Laplace double-layer in 2D, Numerical Linear Algebra with Applications, 2014.
  123. B. Quaife and G. Biros, High-volume fraction simulations of two-dimensional vesicle suspensions, Journal on Computational Physics, 274, 245–267, 2014.
  124. Jennifer Worthen, Georg Stadler, Noemi Petra, Michael Gurnis, and Omar Ghattas, Towards adjoint-based inversion for rheological parameters in nonlinear viscous mantle flow. , Physics of the Earth and Planetary Interiors, 234:23–34, 2014.
  125. Cueto-Felgueroso,Luis and Juanes,Ruben, A phase-field model of two-phase Hele-Shaw flow, Journal of Fluid Mechanics, 758, 2014.
  126. T. Arbogast, D. Estep, B. Sheehan, and S. Tavener, A-posteriori error estimates for mixed finite element and finite volume methods for problems coupled through a boundary with non-matching grids, IMA Journal on Numerical Analysis, doi: 10.1093/imanum/drt049, 2013.
  127. Tan Bui-Thanh and Omar Ghattas, Analysis of the Hessian for inverse scattering problems. Part III: Inverse medium scattering of electromagnetic waves. , Inverse Problems and Imaging, 7(4):1139–1155, 2013.
  128. T. Bui-Thanh, O. Ghattas, J. Martin, and G. Stadler, A computational framework for infinite-dimensional Bayesian inverse problems. Part I: The linearized case, with applications to global seismic inversion, SIAM Journal on Scientific Computing, 35(6):A2494-A2523, 2013.
  129. Tan Bui-Thanh, Leszek Demkowicz, and Omar Ghattas, A unified discontinuous Petrov-Galerkin method and its analysis for Friedrichs’ systems. , SIAM Journal on Numerical Analysis, 51(4):1933–1958, 2013.
  130. Tan Bui-Thanh, Leszek Demkowicz, and Omar Ghattas, Constructively well-posed approximation method with unity inf-sup and continuity constants for partial differential equations. , Mathematics Of Computation, 82(284):1923–1952, 2013.
  131. Troy Butler, Clint Dawson, and Tim Wildey, Propagation of uncertainties using improved surrogate models, SIAM/ASA Journal on Uncertainty Quantification, 1:164–191, 2013.
  132. V. Carey, D. Estep, S. Tavener, A posteriori analysis and adaptive error control for operator decomposition solution of coupled semilinear elliptic systems, International Journal of Numerical Methods in Engineering, 94, 826-849, 2013.
  133. X. Fu, L. Cueto-Felgueroso, and R. Juanes, Pattern formation and coarsening dynamics in three-dimensional convective mixing in porous media, Philosophical Transactions of the Royal Society A, 371, 20120355, 2013.
  134. S. Garashchuk, J. Jakowski, L. Wang, B.G. Sumpter, A Hybrid Quantum Trajectory-Electronic Structure Approach for Exploring Nuclear Effects in the Dynamics of Nanomaterials, J. Chem. Theory & Computation 9, 5221-5235, 2013.
  135. D. Estep. V. Ginting, J. Hameed, and S. Tavener, A posteriori analysis of an iterative multi-discretization method for reaction-diffusion systems, Computer Methods in Applied Mechanics and Engineering, Vol. 267, 1-22, 2013.
  136. H. Gomez, L. Cueto-Felgueroso, and R. Juanes, Three-dimensional simulation of unstable gravity-driven infiltration of water into a porous medium, Journal of Computational Physics, 238, 217--239, 2013.
  137. Juan J. Hidalgo and Christopher W. MacMinn and Ruben Juanes, Dynamics of convective dissolution from a migrating current of carbon dioxide, Advances in Water Resources, 62, Part C, 2013.
  138. K. Ho and L. Ying, Hierarchical interpolative factorization for elliptic operators: integral equations, Communications on Pure and Applied Mathematics, 69(7), 1314-1353, 2013.
  139. K. Ho and L. Ying, Hierarchical interpolative factorization for elliptic operators: differential equations, Communications on Pure and Applied Mathematics, 69(8), 1415-1451, 2013.
  140. M. Iglesias and C. Dawson, The regularizing Levenburg-Marquardt scheme for history matching of petroleum reservoirs, Computational Geosciences, 17(6):1033-1053, 2013.
  141. Ruben Juanes and Holger Class, Special issue on computational methods in geologic CO2 sequestration, Advances in Water Resources, 62, Part C, 2013.
  142. D. E. Keyes, L. C. McInnes, C. Woodward, W. Gropp, E. Myra, M. Pernice, J. Bell, J. Brown, A. Clo, J. Connors, E. Constantinescu, D. Estep, K. Evans, C. Farhat, A. Hakim, G. Hammond, G. Hansen, J. Hill, T. Isaac, X. Jiao, K. Jordan, D. Kaushik, E. Kaxiras, A. Koniges, K. Lee, A. Lott, Q. Lu, J. Magerlein, R. Maxwell, M. McCourt, M. Mehl, R. Pawlowski, A. Peters Randles, D. Reynolds, B. Riviere, U. Ruede, T. Scheibe, J. Shadid, B. Sheehan, M. Shephard, A. Siegel, B. Smith, X. Tang, C. Wilson, and B. Wohlmuth, Multiphysics Simulations: Challenges and Opportunities, International Journal of High Performance Computing Applications, (27), 2013.
  143. C. Lieberman and K. Willcox, Goal-Oriented Inference: Approach, Linear Theory, and Application to Advection Diffusion, SIAM Review, 55-3, 493--519, 2013.
  144. Oden, J.T., Prudencio, E.E., and Hawkins-Daarud, A., Selection and Assessment of Phenomenological Models of Tumor Growth, Mathematical Models and Methods in Applied Sciences, 23 (7), 1309-1338, 2013.
  145. J.T. Oden, E.E. Prudencio, and P.T. Bauman, Virtual model validation of complex multiscale systems: Applications to nonlinear elastostatics, Computer Methods in Applied Mechanics and Engineering, 266:162-184, 2013.
  146. Szulczewski,M. L. and Hesse,M. A. and Juanes,R., Carbon dioxide dissolution in structural and stratigraphic traps, Journal of Fluid Mechanics, 736, 2013.
  147. T. Butler and D. Estep, A numerical method for solving a stochastic inverse problem for parameter, Annals of Nuclear Energy, 86-94, 2012.
  148. L. Cueto-Felgueroso and R. Juanes, Macroscopic phase-field modeling of partial wetting: bubbles in a capillary tube, Physical Review Letters, Vol. 108, 2012.
  149. K. Farrell and J. T. Oden, Statistical calibration and validation methods of coarse-grained and macro models of atomic systems, ICES Technical Report, 12-45, 2012.
  150. Chad Lieberman and Karen Willcox, Goal-Oriented Inference: Approach, Linear Theory, and Application to Advection Diffusion, SIAM Journal on Scientific Computing, 34(4):A1880-A1904, 2012.

Conference Proceedings

  1. Arash Bakhtiari, Dhairya Malhotra, Amir Raoofy, Miriam Mehl, Hans-Joachim Bungartz, and George Biros, A Parallel Aribtrary-Order Accurate AMR Algorithm for the Scalar Advection-Diffusion Equation, Proceedings of the SC2016, The Interna- tional Conference for High Performance Computing, Networking, Storage and Analysis, IEEE/ACM, Salt Lake City, Utah, November, 2016.
  2. Andreas Mang, Amir Gholami and George Biros, Distributed-Memory Large Deformation Diffeomorphic 3D Image Registration, Proceedings of the SC2016, The International Conference for High Performance Computing, Networking, Storage and Analysis, IEEE/ACM, Salt Lake City, Utah, November, 2016.
  3. Johann Rudi, Georg Stadler, and Omar Ghattas, μ-BFBT preconditioner for Stokes flow problems with highly heterogeneous viscosity. , , Winner of the Student Paper Competition at the 14th Copper Mountain Conference on Iterative Methods, Copper Mountain, Colorado, USA, 2016.
  4. Chenhan Yu, William March, and George Biros, INV-ASKIT:A Parallel Fast Direct Solver for Kernel Matrices, 30th IEEE International Parallel & Distributed Processing Symposium (IEEE IPDPS 2016), Chicago, IL, May, 2016.
  5. William B. March, Bo Xiao, Sameer Tharakan, Chenhan D. Yu, and George Biros, A kernel-independent FMM in general dimensions, Proceedings of the SC2015, The International Conference for High Performance Computing, Networking, Storage and Analysis, IEEE/ACM, Austin, Texas, November, 2015.
  6. William B. March, Bo Xiao, Sameer Tharakan, Chenhan D. Yu, and George Biros, Robust treecode approximation for kernel machines, Proceedings of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD15), ACM, Sydney, Australia, August, 2015.
  7. William B. March, Bo Xiao, Chenhan Yu, and George Biros, An algebraic parallel treecode in arbitrary dimensions, Proceedings of the 29th IEEE International Parallel and Distributed Computing Symposium (IPDPS 2015), IEEE, Hyderabad, India, May, 2015.
  8. Johann Rudi, A. Crisiano I. Malossi, Tobin Isaac, Georg Stadler, Michael Gurnis, Yves Ineichen, Costas Bekas, Alessandro Curioni, and Omar Ghattas, An extreme-scale implicit solver for complex PDEs: Highly heterogeneous flow in earth’s mantle. , Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pages 5:1–5:12, 2015.
  9. Hari Sundar and Omar Ghattas, A nested partitioning algorithm for adaptive meshes on heterogeneous clusters. , ACM International Conference on Supercomputing, ICS’15, Newport Beach, CA, 2015.
  10. Chenhan D. Yu, Jianyu Huang, Woody Austin and George Biros, Performance Optimization for the K-Nearest Neighbors Kernel on x86 Architectures, Proceedings of the SC2015, The International Conference for High Performance Computing, Networking, Storage and Analysis, IEEE/ACM, Austin, Texas, November, 2015.
  11. Dhairya Malhotra, Amir Gholami, and George Biros, A volume integral equation Stokes solver for problems with variable coefficients, Proceedings of SC2014, IEEE/ACM, New Orleans, LA, November, (Best Student Paper Finalist, 2014.
  12. B. Quaife and G. Biros, HykSort: a new variant of hypercube quicksort on distributed memory architectures, ACM International Conference on Supercomputing, Eugene, Oregon, 2013.
  13. H. Sundar, D. Malhotra, K. W. Schulz, Algorithms for High-Throughput Disk-to-Disk Sorting, Proceedings of ACM/IEEE SC13, Denver, CO, 2013.

Student Theses

  1. Rebecca E. Morrison, On the representation of model inadequacy: a stochastic operator approach, PhD Dissertation, Univ. Texas at Austin, 2016.
  2. H. Li, Model Adaptivity for Goal-Oriented Inference, Master's Thesis, Aeronautics and Astronautics, Massachusetts Institute of Technology, 2015.
  3. M. Parno, Transport maps for accelerated Bayesian computation, Ph.D. Thesis, Aeronautics and Astronautics, Massachusetts Institute of Technology, 2014.
  4. C. Lieberman, Goal-oriented inference: Theoretical foundations and application to carbon capture and storage, Ph.D. Thesis, Aeronautics and Astronautics, Massachusetts Institute of Technology, 2013.

Books

  1. O. Ghattas, T. Isaac, N. Petra, and G. Stadler, Scalable Algorithms for Bayesian Inference of Large-Scale Models from Large-Scale Data, in High Performance Computing for Computational Science--VECPAR 2016, I. Dutra, R. Camacho, J. Barbosa, O. Marques (eds), Springer Lecture Notes in Computer Science, 10150:3-6, 2017.
  2. Oden, J.T.; Babuska, I.; and Faghihi, D., Predictive Computational Science: Computer Predictions in the Presence of Uncertainty, Encyclopedia of Computational Mechanics, Second Edition, 2017.
  3. K. Ravi-Chandar, D. Faghihi, and JT. Oden, A system for monitoring damage in composite materials using statistical calibrations and Bayesian model selection. , F. Darema (eds.), Data Driven Application Systems. Springer. , 2016.
  4. Strait, Melissa and Shearer, Michael and Levy, Rachel and Cueto-Felgueroso, Luis and Juanes, Ruben, Two Fluid Flow in a Capillary Tube, Collaborative Mathematics and Statistics Research: Topics from the 9th Annual UNCG Regional Mathematics and Statistics Conference, 149--161, 2015.
  5. P.C. Lichtner and S. Karra, Modeling Multiscale-Multiphase-Multicomponent Reactive Flows in Porous Media: Application to CO2 Sequestration and Enhanced Geothermal Energy using PFLOTRAN, Computational Models for CO2 Geo-sequestration & Compressed Air Energy Storage, 81-136, 2014.