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 35 articles )

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  1. 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)
  2. 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)
  3. Tröltzsch, Anke: A sequential quadratic programming algorithm for equality-constrained optimization without derivatives (2016)
  4. Turinsky, Paul J.; Kothe, Douglas B.: Modeling and simulation challenges pursued by the consortium for advanced simulation of light water reactors (CASL) (2016)
  5. 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)
  6. Hadjidoukas, P.E.; Angelikopoulos, P.; Papadimitriou, C.; Koumoutsakos, P.: $\Pi$4U: a high performance computing framework for Bayesian uncertainty quantification of complex models (2015)
  7. 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)
  8. Agarwal, Anshul; Biegler, Lorenz T.: A trust-region framework for constrained optimization using reduced order modeling (2013)
  9. 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)
  10. Rios, Luis Miguel; Sahinidis, Nikolaos V.: Derivative-free optimization: a review of algorithms and comparison of software implementations (2013)
  11. 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)
  12. Constantine, Paul G.; Eldred, Michael S.; Phipps, Eric T.: Sparse pseudospectral approximation method (2012)
  13. Perez, Ruben E.; Jansen, Peter W.; Martins, Joaquim R.R.A.: PyOpt: a python-based object-oriented framework for nonlinear constrained optimization (2012)
  14. Richardson, James N.; Adriaenssens, Sigrid; Bouillard, Philippe; Coelho, Rajan Filomeno: Multiobjective topology optimization of truss structures with kinematic stability repair (2012)
  15. Eldred, Michael S.: Design under uncertainty employing stochastic expansion methods (2011)
  16. Elman, Howard C.; Miller, Christopher W.; Phipps, Eric T.; Tuminaro, Raymond S.: Assessment of collocation and Galerkin approaches to linear diffusion equations with random data (2011)
  17. Knezevic, David J.; Peterson, John W.: A high-performance parallel implementation of the certified reduced basis method (2011)
  18. Krack, Malte; Secanell, Marc; Mertiny, Pierre: Cost optimization of a hybrid composite flywheel rotor with a split-type hub using combined analytical/numerical models (2011) ioport
  19. Lunacek, Monte; Nag, Ambarish; Alber, David M.; Gruchalla, Kenny; Chang, Christopher H.; Graf, Peter A.: Simulation, characterization, and optimization of metabolic models with the high performance systems biology toolkit (2011)
  20. Afonso, S.M.B.; Lyra, P.R.M.; Albuquerque, T.M.M.; Motta, R.S.: Structural analysis and optimization in the framework of reduced-basis method (2010)

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