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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.

, , , and . J. Comput. Phys., (2015)

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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., , , and . J. Comput. Phys., (2015)A Fast and Scalable Method for A-Optimal Design of Experiments for Infinite-dimensional Bayesian Nonlinear Inverse Problems., , , and . SIAM J. Sci. Comput., (2016)Scalable Algorithms for Bayesian Inference of Large-Scale Models from Large-Scale Data., , , and . VECPAR, volume 10150 of Lecture Notes in Computer Science, page 3-6. Springer, (2016)Large deviation theory-based adaptive importance sampling for rare events in high dimensions., and . CoRR, (2022)Discretely Exact Derivatives for Hyperbolic PDE-Constrained Optimization Problems Discretized by the Discontinuous Galerkin Method., , , and . J. Sci. Comput., 63 (1): 138-162 (2015)Plattentektonik im Supercomputer, , and . Spektrum der Wissenschaft,   (4): 12-14 (2011)Extreme-Scale AMR., , , , , , and . SC, page 1-12. IEEE, (2010)A-Optimal Design of Experiments for Infinite-Dimensional Bayesian Linear Inverse Problems with Regularized ℓ0-Sparsification., , , and . SIAM J. Sci. Comput., (2014)Sensitivity Analysis of the Information Gain in Infinite-Dimensional Bayesian Linear Inverse Problems., , , and . CoRR, (2023)Extreme-scale UQ for Bayesian inverse problems governed by PDEs., , , , , and . SC, page 3. IEEE/ACM, (2012)