Dakota: Bridging Advanced Scalable Uncertainty Quantification Algorithms with Production Deployment

Laura P. Swiler (), Michael S. Eldred () and Brian M. Adams ()
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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

Chapter 49 in Handbook of Uncertainty Quantification, 2017, pp 1651-1693 from Springer

Abstract: 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.

Keywords: Dakota software; Open-source software; Black box; Parallel computing; Surrogate models; Sampling; Reliability; Polynomial chaos expansions; Stochastic collocation; Epistemic UQ; Interval estimation; Multifidelity; Stochastic design; Bayesian calibration; Adaptive methods; Sensitivity analysis; Optimization; Calibration; Importance sampling (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/978-3-319-12385-1_52

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