Dakota: Bridging Advanced Scalable Uncertainty Quantification Algorithms with Production Deployment

  • Laura P. Swiler
  • Michael S. Eldred
  • Brian M. Adams
Living reference work entry

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 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 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Laura P. Swiler
    • 1
  • Michael S. Eldred
    • 1
  • Brian M. Adams
    • 1
  1. 1.Optimization and Uncertainty Quantification DepartmentSandia National LaboratoriesAlbuquerqueUSA

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