A Multilevel Parallel Object-Oriented Framework for Design Optimization,Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis, The Dakota (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. (Source: http://plato.asu.edu)

References in zbMATH (referenced in 43 articles )

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  1. Bergmann, Michel; Ferrero, Andrea; Iollo, Angelo; Lombardi, Edoardo; Scardigli, Angela; Telib, Haysam: A zonal Galerkin-free POD model for incompressible flows (2018)
  2. Joseph C. Ferguson, Francesco Panerai, Arnaud Borner, Nagi N. Mansour: PuMA: the Porous Microstructure Analysis software (2018)
  3. Rezaeiravesh, Saleh; Vinuesa, Ricardo; Liefvendahl, Mattias; Schlatter, Philipp: Assessment of uncertainties in hot-wire anemometry and oil-film interferometry measurements for wall-bounded turbulent flows (2018)
  4. Talgorn, Bastien; Audet, Charles; Le Digabel, Sébastien; Kokkolaras, Michael: Locally weighted regression models for surrogate-assisted design optimization (2018)
  5. Nakshatrala, K. B.; Nagarajan, H.; Shabouei, M.: A numerical methodology for enforcing maximum principles and the non-negative constraint for transient diffusion equations (2016)
  6. Oden, J. Tinsley; Lima, Ernesto A. B. F.; Almeida, Regina C.; Feng, Yusheng; Rylander, Marissa Nichole; Fuentes, David; Faghihi, Danial; Rahman, Mohammad M.; DeWitt, Matthew; Gadde, Manasa; Zhou, J. Cliff: Toward predictive multiscale modeling of vascular tumor growth, computational and experimental oncology for tumor prediction (2016)
  7. Shadid, J. N.; Smith, T. M.; Cyr, E. C.; Wildey, T. M.; Pawlowski, R. P.: Stabilized FE simulation of prototype thermal-hydraulics problems with integrated adjoint-based capabilities (2016)
  8. Tröltzsch, Anke: A sequential quadratic programming algorithm for equality-constrained optimization without derivatives (2016)
  9. Turinsky, Paul J.; Kothe, Douglas B.: Modeling and simulation challenges pursued by the consortium for advanced simulation of light water reactors (CASL) (2016)
  10. Wentworth, Mami T.; Smith, Ralph C.; Banks, H. T.: Parameter selection and verification techniques based on global sensitivity analysis illustrated for an HIV model (2016)
  11. Emery, John M.; Field, Richard V. jun.; Foulk, James W. III; Karlson, Kyle N.; Grigoriu, Mircea D.: Predicting laser weld reliability with stochastic reduced-order models (2015)
  12. Hadjidoukas, P. E.; Angelikopoulos, P.; Papadimitriou, C.; Koumoutsakos, P.: $\Pi$4U: a high performance computing framework for Bayesian uncertainty quantification of complex models (2015)
  13. Thompson, A. P.; Swiler, L. P.; Trott, C. R.; Foiles, S. M.; Tucker, G. J.: Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials (2015)
  14. Hingerl, Ferdinand F.; Kosakowski, Georg; Wagner, Thomas; Kulik, Dmitrii A.; Driesner, Thomas: GEMSFIT: a generic fitting tool for geochemical activity models (2014)
  15. Agarwal, Anshul; Biegler, Lorenz T.: A trust-region framework for constrained optimization using reduced order modeling (2013)
  16. Hawkins-Daarud, Andrea; Prudhomme, Serge; van der Zee, Kristoffer G.; Oden, J. Tinsley: Bayesian calibration, validation, and uncertainty quantification of diffuse interface models of tumor growth (2013)
  17. Rios, Luis Miguel; Sahinidis, Nikolaos V.: Derivative-free optimization: a review of algorithms and comparison of software implementations (2013)
  18. Robinson, A. C.; Berry, R. D.; Carpenter, J. H.; Debusschere, B.; Drake, R. R.; Mattsson, A. E.; Rider, W. J.: Fundamental issues in the representation and propagation of uncertain equation of state information in shock hydrodynamics (2013)
  19. Constantine, Paul G.; Eldred, Michael S.; Phipps, Eric T.: Sparse pseudospectral approximation method (2012)
  20. Perez, Ruben E.; Jansen, Peter W.; Martins, Joaquim R. R. A.: PyOpt: a python-based object-oriented framework for nonlinear constrained optimization (2012)

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Further publications can be found at: http://dakota.sandia.gov/publications.html