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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.

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Support for Non-conformal Meshes in PETSc's DMPlex Interface., и . CoRR, (2015)Discontinuous Galerkin methods for plasma physics in the scrape-off layer of tokamaks., , , и . J. Comput. Phys., (2014)Recursive Algorithms for Distributed Forests of Octrees., , , и . SIAM J. Sci. Comput., (2015)An extreme-scale implicit solver for complex PDEs: highly heterogeneous flow in earth's mantle., , , , , , , , , и . SC, стр. 5:1-5:12. ACM, (2015)Low-Cost Parallel Algorithms for 2: 1 Octree Balance., , и . IPDPS, стр. 426-437. IEEE Computer Society, (2012)Scalable Algorithms for Bayesian Inference of Large-Scale Models from Large-Scale Data., , , и . VECPAR, том 10150 из Lecture Notes in Computer Science, стр. 3-6. Springer, (2016)Extreme-Scale AMR., , , , , , и . SC, стр. 1-12. IEEE, (2010)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., , , und . J. Comput. Phys., (2015)Multiphysics simulations, , , , , , , , , und 35 andere Autor(en). The International Journal of High Performance Computing Applications, 27 (1): 4--83 (февраля 2013)Robust expected information gain for optimal Bayesian experimental design using ambiguity sets., und . UAI, Volume 180 von Proceedings of Machine Learning Research, Seite 728-737. PMLR, (2022)