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Scalable Algorithms for Bayesian Inference of Large-Scale Models from Large-Scale Data

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High Performance Computing for Computational Science – VECPAR 2016 (VECPAR 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10150))

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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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Authors and Affiliations

  1. Institute for Computational Engineering and Sciences, Departments of Geological Sciences and Mechanical Engineering, The University of Texas at Austin, Austin, USA

    Omar Ghattas

  2. Computation Institute, University of Chicago, Chicago, USA

    Tobin Isaac

  3. School of Natural Sciences, University of California, Merced, Merced, USA

    Noémi Petra

  4. Courant Institute for Mathematical Sciences, New York University, New York, USA

    Georg Stadler

Authors
  1. Omar Ghattas
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  2. Tobin Isaac
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  3. Noémi Petra
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  4. Georg Stadler
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Corresponding author

Correspondence to Omar Ghattas .

Editor information

Editors and Affiliations

  1. University of Porto, Porto, Portugal

    Inês Dutra

  2. University of Porto, Porto, Portugal

    Rui Camacho

  3. University of Porto, Porto, Portugal

    Jorge Barbosa

  4. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Osni Marques

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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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  • DOI: https://doi.org/10.1007/978-3-319-61982-8_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61981-1

  • Online ISBN: 978-3-319-61982-8

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