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• W2735751036 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." @default.
• W2735751036 created "2017-07-21" @default.
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• W2735751036 date "2017-01-01" @default.
• W2735751036 modified "2023-09-27" @default.
• W2735751036 title "Scalable Algorithms for Bayesian Inference of Large-Scale Models from Large-Scale Data" @default.
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• W2735751036 doi "https://doi.org/10.1007/978-3-319-61982-8_1" @default.
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