DAKOTA

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 52 articles )

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  1. Clerx, M., Robinson, M., Lambert, B., Lei, C.L., Ghosh, S., Mirams, G.R. and Gavaghan, D.J.: Probabilistic Inference on Noisy Time Series (PINTS) (2019) not zbMATH
  2. De Donno, Remo; Ghidoni, Antonio; Noventa, Gianmaria; Rebay, Stefano: Shape optimization of the ERCOFTAC centrifugal pump impeller using open-source software (2019)
  3. Gladish, Daniel W.; Darnell, Ross; Thorburn, Peter J.; Haldankar, Bhakti: Emulated multivariate global sensitivity analysis for complex computer models applied to agricultural simulators (2019)
  4. Katherine R. Barnhart, Eric Hutton, Gregory E. Tucker: umami: A Python package for Earth surface dynamics objective function construction (2019) not zbMATH
  5. Bergmann, Michel; Ferrero, Andrea; Iollo, Angelo; Lombardi, Edoardo; Scardigli, Angela; Telib, Haysam: A zonal Galerkin-free POD model for incompressible flows (2018)
  6. Butler, T.; Jakeman, J.; Wildey, T.: Convergence of probability densities using approximate models for forward and inverse problems in uncertainty quantification (2018)
  7. Joseph C. Ferguson, Francesco Panerai, Arnaud Borner, Nagi N. Mansour: PuMA: the Porous Microstructure Analysis software (2018) not zbMATH
  8. Kasia Sawicka, Gerard B.M. Heuvelink, Dennis J.J. Walvoort: Spatial Uncertainty Propagation Analysis with the spup R Package (2018) not zbMATH
  9. Latif, Majid jun.; May, Elebeoba E.: A multiscale agent-based model for the investigation of E. coli K12 metabolic response during biofilm formation (2018)
  10. Martínez-Frutos, Jesús; Periago Esparza, Francisco: Optimal control of PDEs under uncertainty. An introduction with application to optimal shape design of structures (2018)
  11. 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)
  12. Talgorn, Bastien; Audet, Charles; Le Digabel, Sébastien; Kokkolaras, Michael: Locally weighted regression models for surrogate-assisted design optimization (2018)
  13. Nakshatrala, K. B.; Nagarajan, H.; Shabouei, M.: A numerical methodology for enforcing maximum principles and the non-negative constraint for transient diffusion equations (2016)
  14. 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)
  15. 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)
  16. Tröltzsch, Anke: A sequential quadratic programming algorithm for equality-constrained optimization without derivatives (2016)
  17. Turinsky, Paul J.; Kothe, Douglas B.: Modeling and simulation challenges pursued by the consortium for advanced simulation of light water reactors (CASL) (2016)
  18. 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)
  19. 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)
  20. Hadjidoukas, P. E.; Angelikopoulos, P.; Papadimitriou, C.; Koumoutsakos, P.: (\Pi)4U: a high performance computing framework for Bayesian uncertainty quantification of complex models (2015)

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