References
[1] Alen Alexanderian, Philip J. Gloor, and Omar Ghattas. On Bayesian A-and D-optimal experimental designs in infinite dimensions. Bayesian Analysis, 2016.
[2] 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.
[3] 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.
[4] 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, 2016. Submitted, arXiv:1602.07592.
[5] Boian S Alexandrov and Velimir V Vesselinov. Blind source separation for groundwater pressure analysis based on nonnegative matrix factorization. Water Resources Research, 50:7332–7347, 2014.
[6] 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, 2016. Submitted.
[7] Amir Alizadeh Pahlavan, Luis Cueto-Felgueroso, Gareth H. McKinley, and Ruben Juanes. Thin films in partial wetting: Internal selection of contact-line dynamics. Phys. Rev. Lett., 115:034502, Jul 2015.
[8] S. Allu, B. Velamur Asokan, W.A. Shelton, B. Philip, and S. Pannala. A generalized multi-dimensional mathematical model for charging and discharging processes in a supercapacitor. Journal of Power Sources, 256:369 – 382, 2014.
[9] 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.
[10] Arash Bakhtiari, Dhairya Malhotra, Amir Raoofy, Miriam Mehl, Hans-Joachim Bungartz, and George Biros. A parallel arbitrary-order accurate AMR algorithm for the scalar advection-diffusion equation. In Proceedings of SC16, The SCxy Conference series, Salt lake City, Utah, November 2016. ACM/IEEE.
[11] Amir Gholami George Biros. AccFFT: A library for distributed-memory FFT on CPU and GPU architectures. SIAM Journal on Scientific Computing, 38(3):C280–C306, 2016.
[12] 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.
[13] 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.
[14] 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.
[15] 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.
[16] 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.
[17] 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.
[18] Tan Bui-Thanh, Omar Ghattas, James Martin, and Georg Stadler. A computational framework for infinite-dimensional Bayesian inverse problems Part I: The linearized case, with application to global seismic inversion. SIAM Journal on Scientific Computing, 35(6):A2494–A2523, 2013.
[19] T. Butler, D. Estep, S. Tavener, C. Dawson, and 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.
[20] 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, 78:6079, 2015.
[21] 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, Vol. 39, No. 5, pp. A2072-A2098, 2017.
[22] T. Butler, A. Huhtala, and M. Juntunen. Quantifying uncertainty in material damage from vibrational data. Journal of Computational Physics, 283:414 – 435, 2015.
[23] Troy Butler, Clint Dawson, and Tim Wildey. Propagation of uncertainties using improved surrogate models. SIAM/ASA Journal on Uncertainty Quantification, 1:164–191, 2013.
[24] 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, Vol. 54, No. 5, pp. 2974-3002, 2016.
[25] J.H. Chaudhry, D. Estep, and M. Gunzburger. Exploration of efficient reduced-order modeling and a posteriori error estimation. International Journal for Numerical Methods in Engineering, Vol. 111, No. 2, pp. 103-122, 2017.
[26] 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.
[27] 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.
[28] Jane Y. Y. Chui, Pietro de Anna, and Ruben Juanes. Interface evolution during radial miscible viscous fingering. Phys. Rev. E, 92:041003, Oct 2015.
[29] 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, 2014.
[30] 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.
[31] Benjamin Crestel, Alen Alexanderian, Georg Stadler, and Omar Ghattas. A-optimal encoding weights for nonlinear inverse problems, with application to the Helmholtz inverse problem. SIAM Journal on Scientific Computing, 2016. Submitted.
[32] Luis Cueto-Felgueroso and Ruben Juanes. Macroscopic phase-field model of partial wetting: Bubbles in a capillary tube. Phys. Rev. Lett., 108:144502, Apr 2012.
[33] Luis Cueto-Felgueroso and Ruben Juanes. A phase-field model of two-phase Hele-Shaw flow. Journal of Fluid Mechanics, 758:522–552, Nov 2014.
[34] Luis Cueto-Felgueroso and Ruben Juanes. A discrete-domain description of multiphase flow in porous media: Rugged energy landscapes and the origin of hysteresis. Geophysical Research Letters, 43(4):1615–1622, 2016. 2015GL067015.
[35] T. Cui, K. J. H. Law, and Y. M. Marzouk. Dimension-independent likelihood-informed MCMC. Journal of Computational Physics, 304:109–137, 2016.
[36] T. Cui, J. Martin, Y. M. Marzouk, A. Solonen, and A. Spantini. Likelihood-informed dimension reduction for nonlinear inverse problems. Inverse Problems, 30:114015, 2014.
[37] T. Cui, 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.
[38] T. Cui, 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.
[39] A. Damle, L. Lin, and L. Ying. SCDM-k: Localized orbitals for solids via selected columns of the density matrix. ArXiv e-prints, July 2015.
[40] A. Damle, L. Lin, and L. Ying. Accelerating selected columns of the density matrix computations via approximate column selection. ArXiv e-prints, April 2016.
[41] Anil Damle, Lin Lin, and Lexing Ying. Compressed representation of kohn-sham orbitals via selected columns of the density matrix. Journal of Chemical Theory and Computation, 11(4):1463–1469, 2015.
[42] P. de Anna, B. Quaife, G. Biros, and R. Juanes. Porelets: Prediction of velocity distribution from pore structure in simple porous media. Physical Review Letters, 2016. Submitted.
[43] Yalchin Efendiev and Michael Presho. Multiscale model reduction with generalized multiscale finite element methods in geomathematics. In W.H. Freeden et al, editor, Handbook of Geomathematics, pages 679–699. Springer-Verlag, 2015.
[44] D. Elfverson, D. Estep, F. Hellman, and A. Malqvist. Uncertainty quantification for approximate p-quantiles for physical models with stochastic inputs. SIAM ASA Journal on Uncertainty Quantification, 2:826850, 2014.
[45] K. Farrell and J.T. Oden. Calibration and validation of coarse-grained models of atomic systems: Application to semiconductor manufacturing. Journal of Computational Mechanics, 54:3–19, 2014.
[46] K Farrell, JT Oden, and D Faghihi. A Bayesian framework for adaptive selection, calibration, and validation of coarse-grained models of atomistic systems. Journal of Computational Physics, 295:189–208, 2015.
[47] K Farrell-Maupin and JT Oden. Adaptive selection and validation of models of complex systems in the presence of uncertainty. Journal of Research in Mathematical Sciences, 2016. Submitted.
[48] Chi Feng and Youssef Marzouk. Focused Bayesian experimental design using layered multiple importance sampling. preprint, 2016.
[49] X. Fu, L. Cueto-Felgueroso, D. Bolster, and R. Juanes. Rock dissolution patterns and geochemical shutdown of co2–brine–carbonate reactions during convective mixing in porous media. Journal of Fluid Mechanics, 764:296–315, 2 2015.
[50] X. Fu, L. Cueto-Felgueroso, and R. Juanes. Thermodynamic coarsening arrested by viscous fingering in partially-miscible binary mixtures. Physical Review E. Accepted, in press.
[51] Xiaojing Fu, Luis Cueto-Felgueroso, and Ruben Juanes. Pattern formation and coarsening dynamics in three-dimensional convective mixing in porous media. Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 371(2004), 2013.
[52] Vikram V. Garg, Luis Tenorio, and Karen Willcox. Minimum local distance density estimation. Communications in Statistics - Theory and Methods, 0(ja):0–0, 0.
[53] Hector Gomez, Luis Cueto-Felgueroso, and Ruben Juanes. Three-dimensional simulation of unstable gravity-driven infiltration of water into a porous medium. Journal of Computational Physics, 238:217 – 239, 2013.
[54] Brad T. Gooch, Sasha P. Carter, Omar Ghattas, Duncan A Young, and Donald D. Blankenship. Possible groundwater dominance in the subglacial hydrology of ice sheet interiors: example at Dome C, East Antarctica. The Cryosphere, 2016. Submitted.
[55] Lindley Graham, Steven Mattis, Scott Walsh, Troy Butler, Michael Pilosov, and Damon McDougall. Bet: Butler, estep, tavener method v2.0.0, August 2016.
[56] Matthew Grasinger, Daniel O’Malley, Velimir Vesselinov, and Satish Karra. Decision analysis for robust CO2 injection: Application of Bayesian-Information-Gap Decision Theory. International Journal of Greenhouse Gas Control, 49:73–80, 2016.
[57] Juan J. Hidalgo, Christopher W. MacMinn, and Ruben Juanes. Dynamics of convective dissolution from a migrating current of carbon dioxide. Advances in Water Resources, 62, Part C:511 – 519, 2013. Computational Methods in Geologic CO2 Sequestration.
[58] Kenneth L. Ho and Lexing Ying. Hierarchical interpolative factorization for elliptic operators: Differential equations. Communications on Pure and Applied Mathematics, 69(8):1415–1451, 2016.
[59] Kenneth L. Ho and Lexing Ying. Hierarchical interpolative factorization for elliptic operators: integral equations. Comm. Pure Appl. Math., 69(7):1314–1353, 2016.
[60] Xun Huan and Youssef M. Marzouk. Simulation-based optimal Bayesian experimental design for nonlinear systems. J. Comput. Phys., 232(1):288–317, 2013.
[61] Marco Iglesias and Clint Dawson. The regularizing Levenberg-Marquardt scheme for history matching of petroleum reservoirs. Computational Geosciences, 17:1033–1053, 2013.
[62] 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, September 2015.
[63] 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.
[64] 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, 287:150171, 2015.
[65] Ruben Juanes and Holger Class. Special issue on computational methods in geologic co2 sequestration. Advances in Water Resources, 62, Part C:353 – 355, 2013. Computational Methods in Geologic CO2 Sequestration.
[66] P. K. Kang, S. Brown, and R. Juanes. Emergence of anomalous transport in stressed rough fractures. Earth and Planetary Science Letters, Accepted, in press.
[67] P. K. Kang, M. Dentz, T. Le Borgne, S. Lee, and R. Juanes. Anomalous transport in disordered fracture networks: Evolution of the lagrangian velocity distribution and CTRW model for arbitrary injection modes. Submitted for publication.
[68] Peter K. Kang, Pietro de Anna, Joao P. Nunes, Branko Bijeljic, Martin J. Blunt, and Ruben Juanes. Pore-scale intermittent velocity structure underpinning anomalous transport through 3-d porous media. Geophysical Research Letters, 41(17):6184–6190, 2014.
[69] Peter K. Kang, Marco Dentz, Tanguy Le Borgne, and Ruben Juanes. Anomalous transport on regular fracture networks: Impact of conductivity heterogeneity and mixing at fracture intersections. Phys. Rev. E, 92:022148, Aug 2015.
[70] Peter K. Kang, Tanguy Le Borgne, Marco Dentz, Olivier Bour, and Ruben Juanes. Impact of velocity correlation and distribution on transport in fractured media: Field evidence and theoretical model. Water Resources Research, 51(2):940–959, 2015.
[71] H. Li. Model adaptivity for goal-oriented inference. Master’s thesis, Massachusetts Institute of Technology, June 2015.
[72] Y. Li, H. Yang, and L. Ying. Multidimensional Butterfly Factorization. ArXiv e-prints, September 2015.
[73] Y. Li and L. Ying. Distributed-memory Hierarchical Interpolative Factorization. ArXiv e-prints, July 2016.
[74] Yingzhou Li, Haizhao Yang, Eileen R. Martin, Kenneth L. Ho, and Lexing Ying. Butterfly factorization. Multiscale Model. Simul., 13(2):714–732, 2015.
[75] Yingzhou Li, Haizhao Yang, and Lexing Ying. A multiscale butterfly algorithm for multidimensional Fourier integral operators. Multiscale Model. Simul., 13(2):614–631, 2015.
[76] C. Lieberman. Goal-oriented inference: Theoretical foundations and application to carbon capture and storage. PhD thesis, Massachusetts Institute of Technology, June 2013.
[77] 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.
[78] E.A.B.F Lima, J.T. Oden, D.A. Hormuth, T.E. Yankeelov, and R.C Almeida. Selection, calibration, and validation of models of tumor growth. Mathematical Models and Methods in Applied Sciences, 2016. Submitted.
[79] L. Lin, Z. Xu, and L. Ying. Adaptively compressed polarizability operator for accelerating large scale ab initio phonon calculations. ArXiv e-prints, May 2016.
[80] Youzuo Lin, Daniel O’Malley, and Velimir V Vesselinov. A computationally efficient parallel Levenberg-Marquardt algorithm for highly parameterized inverse model analyses. Water Resources Research, pages 1–70, aug 2016.
[81] F. Liu and L. Ying. Localized Sparsifying Preconditioner. ArXiv e-prints, July 2016.
[82] Fei Liu and Lexing Ying. Additive sweeping preconditioner for the helmholtz equation. Multiscale Modeling & Simulation, 14(2):799–822, 2016.
[83] Fei Liu and Lexing Ying. Recursive sweeping preconditioner for the three-dimensional helmholtz equation. SIAM Journal on Scientific Computing, 38(2):A814–A832, 2016.
[84] Jianfeng Lu and Lexing Ying. Compression of the electron repulsion integral tensor in tensor hypercontraction format with cubic scaling cost. J. Comput. Phys., 302:329–335, 2015.
[85] Jianfeng Lu and Lexing Ying. Fast algorithm for periodic density fitting for bloch waves. Annals of Mathematical Sciences and Applications, 1(2):321 – 339, 2016.
[86] Jianfeng Lu and Lexing Ying. Sparsifying preconditioner for soliton calculations. Journal of Computational Physics, 315:458 – 466, 2016.
[87] Dhairya Malhotra and George Biros. PVFMM: A parallel kernel independent FMM for particle and volume potentials. Communications in Computational Physics, 18(03):808–830, 2015.
[88] Dhairya Malhotra and George Biros. Algorithm 967: A distributed-memory fast multipole method for volume potentials. ACM Transactions on Mathematical Software, 43(2):17:1–17:27, 2016.
[89] Dhairya Malhotra, Amir Gholami, and George Biros. A volume integral equation Stokes solver for problems with variable coefficients. In Proceedings of SC14, The SCxy Conference series, New Orleans, Louisiana, November 2014. ACM/IEEE.
[90] Andreas Mang and George Biros. An inexact Newton-Krylov algorithm for constrained diffeomorphic image registration. SIAM Journal on Imaging Sciences, 8(2):1030–1069, 2015.
[91] Andreas Mang and George Biros. Constrained h1-regularization schemes for diffeomorphic image registration. SIAM Journal on Imaging Sciences, 9(3):1854–1194, 2016.
[92] Andreas Mang, Amir Gholami, and George Biros. Distributed-memory large deformation diffeomorphic 3d image registration. In Proceedings of SC16, The SCxy Conference series, Salt lake City, Utah, November 2016. ACM/IEEE.
[93] William B March and George Biros. Far-field compression for fast kernel summation methods in high dimensions. Applied and Computational Harmonic Analysis, 2015.
[94] William B. March, Bo Xiao, and George Biros. ASKIT: Approximate skeletonization kernel-independent treecode in high dimensions. SIAM Journal on Scientific Computing, 37(2):1089–1110, 2015.
[95] S. A. Mattis, T. D. Butler, C. N. Dawson, D. Estep, and V. V. Vesselinov. Parameter estimation and prediction for groundwater contamination based on measure theory. Water Resources Research, 51(9):7608–7629, sep 2015.
[96] Steven A Mattis, Troy D Butler, Clint N Dawson, Don Estep, and Velimir V Vessilinov. Parameter estimation and prediction for groundwater contamination based on measure theory. Water Resources Research, 51:7608–7629, 2015.
[97] V. Minden, A. Damle, K. L. Ho, and L. Ying. Fast spatial Gaussian process maximum likelihood estimation via skeletonization factorizations. ArXiv e-prints, March 2016.
[98] Victor Minden, Anil Damle, Kenneth L. Ho, and Lexing Ying. A technique for updating hierarchical skeletonization-based factorizations of integral operators. Multiscale Modeling & Simulation, 14(1):42–64, 2016.
[99] Christos Nicolaides, Birendra Jha, Luis Cueto-Felgueroso, and Ruben Juanes. Impact of viscous fingering and permeability heterogeneity on fluid mixing in porous media. Water Resources Research, 51(4):2634–2647, 2015.
[100] Christos Nicolaides, Ruben Juanes, and Luis Cueto-Felgueroso. Self-organization of network dynamics into local quantized states. Scientific Reports, 6:21360, 2016.
[101] JT Oden, K Farrell, and D Faghihi. Estimation of error in observables of coarse-grained models of atomic systems. Advanced Modeling and Simulation in Engineering Sciences, 2(5), 2015.
[102] D O’Malley and V Vesselinov. A Combined Probabilistic/Nonprobabilistic Decision Analysis for Contaminant Remediation. SIAM/ASA Journal on Uncertainty Quantification, 2(1):607–621, 2014.
[103] D. O’Malley and V. V. Vesselinov. Analytical solutions for anomalous dispersion transport. Advances in Water Resources, 68:13–23, 2014.
[104] D. O’Malley and V. V. Vesselinov. Groundwater remediation using the information gap decision theory. Water Resources Research, 50(1):246–256, jan 2014.
[105] D. O’Malley and V. V. Vesselinov. Bayesian-information-gap decision theory with an application to CO2 sequestration. Water Resources Research, 51(9):7080–7089, 2015.
[106] M. Opgenoord, D. Allaire, and K. Willcox. Variance-based sensitivity analysis to support simulation-based design under uncertainty. Journal of Mechanical Design, Vol. 138, No. 11, 111410, 2016.
[107] M. Parno. Transport maps for accelerated Bayesian computation. PhD thesis, Massachusetts Institute of Technology, October 2014.
[108] M. Parno and Y. M. Marzouk. Transport map accelerated Markov chain Monte Carlo, 2014. Submitted, arXiv:1412.5492.
[109] M. Parno, T. Moselhy, and Y. M. Marzouk. A multiscale strategy for Bayesian inference using transport maps. SIAM/ASA Journal on Uncertainty Quantification, 2016. in press.
[110] B. Peherstorfer, T. Cui, Y. M. Marzouk, and K. E. Willcox. Multifidelity importance sampling. Computer Methods in Applied Mechanics and Engineering, 300:490–509, 2016.
[111] B. Peherstorfer and K. Willcox. Data-driven operator inference for nonintrusive projection-based model reduction. Computer Methods in Applied Mechanics and Engineering, 306:196–215, 2016.
[112] B. Peherstorfer, K. Willcox, and M. Gunzburger. Optimal model management for multifidelity monte carlo estimation. SIAM Journal on Scientific Computing, 2016.
[113] B. Peherstorfer, K. Willcox, and M. Gunzburger. Survey of multifidelity methods in uncertainty propagation, inference, and optimization. Technical report, Aerospace Computational Design Laboratory, Massachusetts Institute of Technology, 2016.
[114] 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.
[115] M Presho and 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.
[116] M Presho and S Ye. Reduced-order multiscale modeling of nonlinear p-laplacian flows in high-contrast media. Computational Geosciences, 19:921–932, 2015.
[117] E. Qian, M. Grepl, K. Veroy, and K. Willcox. A certified trust region reduced basis approach to PDE-constrained optimization. Technical report, Aerospace Computational Design Laboratory, Massachusetts Institute of Technology, 2016.
[118] Bryan Quaife and George Biros. High-volume fraction of simulations of two-dimensional vesicle suspensions. Journal on Computational Physics, 274:245–267, 2014.
[119] Abtin Rahimian, Shravan K. Veerapaneni, Denis Zorin, and George Biros. Boundary integral method for the flow of vesicles with viscosity contrast in three dimensions. Journal of Computational Physics, 298:766–786, 2015.
[120] K Ravi-Chandar, D Faghihi, and JT Oden. A system for monitoring damage in composite materials using statistical calibrations and Bayesian model selection. In F. Darema, editor, Data Driven Application Systems. Springer, 2016.
[121] J Ren and M Presho. A generalized multiscale finite element method for high-contrast single-phase flow problems in anisotropic media. Journal of Computational and Applied Mathematics, 277:202–214, 2015.
[122] 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. In SC15: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pages 5:1–5:12. ACM, 2015.
[123] 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.
[124] Johann Rudi, Georg Stadler, and Omar Ghattas. Weighted BFBT preconditioner for Stokes flow problems with highly heterogeneous viscosity. SIAM Journal on Scientific Computing, 2016. Submitted.
[125] Ali Samii, Craig Michoski, and Clint Dawson. A parallel and adaptive hybridized discontinuous galerkin method for anisotropic nonhomogeneous diffusion. Computer Methods in Applied Mechanics and Engineering, 304:118–139, 2016.
[126] A. Solonen, T. Cui, J. Hakkarainen, and Y. M. Marzouk. On dimension reduction in Gaussian filters. Inverse Problems, 32(4):045003, 2016.
[127] A. Spantini, T. Cui, K. E. Willcox, L. Tenorio, and Y. M. Marzouk. Goal-oriented optimal approximations of Bayesian linear inverse problems. SIAM Journal on Scientific Computing, 2016. Submitted.
[128] A. Spantini, A. Solonen, T. Cui, J. Martin, L. Tenorio, and Y. M. Marzouk. Optimal low-rank approximation of linear Bayesian inverse problems. SIAM Journal on Scientific Computing, 37:A2451–A2487, 2015.
[129] Melissa Strait, Michael Shearer, Rachel Levy, Luis Cueto-Felgueroso, and Ruben Juanes. Two fluid flow in a capillary tube. In Jan Rychtář, Maya Chhetri, Sat Gupta, and Ratnasingham Shivaji, editors, Collaborative Mathematics and Statistics Research: Topics from the 9th Annual UNCG Regional Mathematics and Statistics Conference, pages 149–161. Springer International Publishing, 2015.
[130] Hari Sundar and Omar Ghattas. A nested partitioning algorithm for adaptive meshes on heterogeneous clusters. In ACM International Conference on Supercomputing, ICS’15, Newport Beach, CA, 2015.
[131] M. L. Szulczewski, M. A. Hesse, and R. Juanes. Carbon dioxide dissolution in structural and stratigraphic traps. Journal of Fluid Mechanics, 736:287–315, 12 2013.
[132] Velimir Vesselinov, Dan O Malley, and Danny Katzman. Model-Assisted Decision Analyses Related to a Chromium Plume at Los Alamos National Laboratory. In WMSYM2015, Phoenix, Arizona, USA, 2015.
[133] Velimir V. Vesselinov, David Broxton, Kay Birdsell, Steven Reneau, Dylan R. Harp, Phoolendra Kumar Mishra, Danny Katzman, TIm Goering, David Vaniman, Pat Longmire, June Fabryka-Martin, Jeff Heikoop, Mei Ding, Don Hickmott, and Elaine Jacobs. Data and Model-Driven Decision Support for Environmental Management of a Chromium Plume at Los Alamos National Laboratory. In WMSYM2013, Phoenix, Arizona, USA, 2013.
[134] Velimir V. Vesselinov, Daniel O’Malley, and Danny Katzman. Robust Decision Analysis for Environmental Management of Groundwater Contamination Sites. In Vulnerability, Uncertainty, and Risk, pages 1970–1979, Reston, VA, jun 2014. American Society of Civil Engineers.
[135] Velimir V Vesselinov, Daniel O’Malley, and Danny Katzman. ZEM: Integrated Framework for Real-Time Data and Model Analyses for Robust Environmental Management Decision Making. In WMSYM 2016, Phoenix, Arizona, USA, 2016.
[136] K. Wang, T. Bui-Thanh, and O. Ghattas. A randomized maximum a posteriori method for posterior sampling of high dimensional nonlinear Bayesian inverse problems. Submitted, 2016.
[137] 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.
[138] 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.
[139] H. Yang, J. Lu, W. P. Brown, I. Daubechies, and L. Ying. Quantitative canvas weave analysis using 2-d synchrosqueezed transforms: Application of time-frequency analysis to art investigation. IEEE Signal Processing Magazine, 32(4):55–63, July 2015.
[140] Haizhao Yang, Jianfeng Lu, and Lexing Ying. Crystal image analysis using 2D synchrosqueezed transforms. Multiscale Model. Simul., 13(4):1542–1572, 2015.
[141] Lexing Ying. Tensor Network Skeletonization. Multiscale Model. Simul., Vol. 15, No. 4, pp. 1423-1447, 2017.
[142] Lexing Ying. Directional preconditioner for 2D high frequency obstacle scattering. Multiscale Model. Simul., 13(3):829–846, 2015.
[143] Lexing Ying. Fast directional computation of high frequency boundary integrals via local FFTs. Multiscale Model. Simul., 13(1):423–439, 2015.
[144] Lexing Ying. Sparsifying preconditioner for pseudospectral approximations of indefinite systems on periodic structures. Multiscale Model. Simul., 13(2):459–471, 2015.
[145] Lexing Ying. Sparsifying preconditioner for the Lippmann-Schwinger equation. Multiscale Model. Simul., 13(2):644–660, 2015.
[146] Chenhan Yu, William March, Bo Xiao, and George Biros. INV-ASKIT: a parallel fast direct solver for kernel matrices. In 30th IEEE International Parallel & Distributed Processing Symposium (IEEE IPDPS 2016), Chicago, USA, May 2016.
[147] 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.
[148] R. Zimmermann and K. Willcox. An accelerated greedy missing point estimation procedure. SIAM Journal on Scientific Computing, Vol. 38, No. 5, A2827-A2850, 2016.