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 64 articles )

Showing results 1 to 20 of 64.
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  1. Degen, Denise; Veroy, Karen; Wellmann, Florian: Certified reduced basis method in geosciences. Addressing the challenge of high-dimensional problems (2020)
  2. Fleeter, Casey M.; Geraci, Gianluca; Schiavazzi, Daniele E.; Kahn, Andrew M.; Marsden, Alison L.: Multilevel and multifidelity uncertainty quantification for cardiovascular hemodynamics (2020)
  3. Girardi, Maria; Padovani, Cristina; Pellegrini, Daniele; Porcelli, Margherita; Robol, Leonardo: Finite element model updating for structural applications (2020)
  4. Liu, Ning; Fu, Li-Yun; Tang, Gang; Kong, Yue; Xu, Xiao-Yi: Modified LSM for size-dependent wave propagation: comparison with modified couple stress theory (2020)
  5. Markus Frings, Norbert Hosters, Corinna Müller, Max Spahn, Christoph Susen, Konstantin Key, Stefanie Elgeti: SplineLib: A Modern Multi-Purpose C++ Spline Library (2020) arXiv
  6. Sauk, Benjamin; Ploskas, Nikolaos; Sahinidis, Nikolaos: GPU parameter tuning for tall and skinny dense linear least squares problems (2020)
  7. Tillmann Muhlpfordt, Frederik Zahn, Veit Hagenmeyer, Timm Faulwasser: PolyChaos.jl - A Julia Package for Polynomial Chaos in Systems and Control (2020) arXiv
  8. Vanslette, Kevin; Al Alsheikh, Abdullatif; Youcef-Toumi, Kamal: Why simple quadrature is just as good as Monte Carlo (2020)
  9. Zachary del Rosario: Grama: A Grammar of Model Analysis (2020) not zbMATH
  10. 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
  11. De Donno, Remo; Ghidoni, Antonio; Noventa, Gianmaria; Rebay, Stefano: Shape optimization of the ERCOFTAC centrifugal pump impeller using open-source software (2019)
  12. Gladish, Daniel W.; Darnell, Ross; Thorburn, Peter J.; Haldankar, Bhakti: Emulated multivariate global sensitivity analysis for complex computer models applied to agricultural simulators (2019)
  13. Katherine R. Barnhart, Eric Hutton, Gregory E. Tucker: umami: A Python package for Earth surface dynamics objective function construction (2019) not zbMATH
  14. Rezaeiravesh, S.; Mukha, T.; Liefvendahl, M.: Systematic study of accuracy of wall-modeled large eddy simulation using uncertainty quantification techniques (2019)
  15. Bergmann, Michel; Ferrero, Andrea; Iollo, Angelo; Lombardi, Edoardo; Scardigli, Angela; Telib, Haysam: A zonal Galerkin-free POD model for incompressible flows (2018)
  16. Butler, T.; Jakeman, J.; Wildey, T.: Convergence of probability densities using approximate models for forward and inverse problems in uncertainty quantification (2018)
  17. Joseph C. Ferguson, Francesco Panerai, Arnaud Borner, Nagi N. Mansour: PuMA: the Porous Microstructure Analysis software (2018) not zbMATH
  18. Kasia Sawicka, Gerard B.M. Heuvelink, Dennis J.J. Walvoort: Spatial Uncertainty Propagation Analysis with the spup R Package (2018) not zbMATH
  19. Latif, Majid jun.; May, Elebeoba E.: A multiscale agent-based model for the investigation of E. coli K12 metabolic response during biofilm formation (2018)
  20. Martínez-Frutos, Jesús; Periago Esparza, Francisco: Optimal control of PDEs under uncertainty. An introduction with application to optimal shape design of structures (2018)

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