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Dakota: Bridging Advanced Scalable Uncertainty Quantification Algorithms with Production Deployment

In: Handbook of Uncertainty Quantification

Author

Listed:
  • Laura P. Swiler

    (Sandia National Laboratories, Optimization and Uncertainty Quantification Department)

  • Michael S. Eldred

    (Sandia National Laboratories, Optimization and Uncertainty Quantification Department)

  • Brian M. Adams

    (Sandia National Laboratories, Optimization and Uncertainty Quantification Department)

Abstract

This chapter highlights uncertainty quantification (UQ) methods in Sandia National Laboratories’ Dakota software. The UQ methods primarily focus on forward propagation of uncertainty, but inverse propagation with Bayesian calibration Calibration is also discussed. The chapter begins with a brief Dakota history and mechanics of licensing, software and documentation acquisition, and getting started, including interfacing simulations to Dakota. Early sections are devoted to core sampling, stochastic expansion, reliability, and epistemic methods, while subsequent sections discuss more advanced capabilities such as mixed epistemic-aleatory UQ, multifidelity UQ, optimization under uncertainty, and Bayesian calibration. The chapter concludes with usage guidelines and a discussion of future directions.

Suggested Citation

  • Laura P. Swiler & Michael S. Eldred & Brian M. Adams, 2017. "Dakota: Bridging Advanced Scalable Uncertainty Quantification Algorithms with Production Deployment," Springer Books, in: Roger Ghanem & David Higdon & Houman Owhadi (ed.), Handbook of Uncertainty Quantification, chapter 49, pages 1651-1693, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-12385-1_52
    DOI: 10.1007/978-3-319-12385-1_52
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