Abstract
One of the greatest challenges in computational science and engineering today is how to combine complex data with complex models to create better predictions. This challenge cuts across every application area within CS&E, from geosciences, materials, chemical systems, biological systems, and astrophysics to engineered systems in aerospace, transportation, structures, electronics, biomedicine, and beyond. Many of these systems are characterized by complex nonlinear behavior coupling multiple physical processes over a wide range of length and time scales. Mathematical and computational models of these systems often contain numerous uncertain parameters, making high-reliability predictive modeling a challenge. Rapidly expanding volumes of observational data—along with tremendous increases in HPC capability—present opportunities to reduce these uncertainties via solution of large-scale inverse problems.
This work was supported by AFOSR grants FA9550-12-1-0484 and FA9550-09-1-0608, DARPA/ARO contract W911NF-15-2-0121, DOE grants DE-SC0010518, DE-SC0009286, DE-11018096, DE-SC0006656, DE-SC0002710, and DE-FG02-08ER25860, and NSF grants ACI-1550593, CBET-1508713, CBET-1507009, CMMI-1028889, and ARC-0941678. Computations were performed on supercomputers at TACC, ORNL, and LLNL. We gratefully acknowledge this support.
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Ghattas, O., Isaac, T., Petra, N., Stadler, G. (2017). Scalable Algorithms for Bayesian Inference of Large-Scale Models from Large-Scale Data. In: Dutra, I., Camacho, R., Barbosa, J., Marques, O. (eds) High Performance Computing for Computational Science – VECPAR 2016. VECPAR 2016. Lecture Notes in Computer Science(), vol 10150. Springer, Cham. https://doi.org/10.1007/978-3-319-61982-8_1
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