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 68 articles )

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  1. Wang, Kun; Sun, WaiChing; Du, Qiang: A non-cooperative meta-modeling game for automated third-party calibrating, validating and falsifying constitutive laws with parallelized adversarial attacks (2021)
  2. Degen, Denise; Veroy, Karen; Wellmann, Florian: Certified reduced basis method in geosciences. Addressing the challenge of high-dimensional problems (2020)
  3. Fleeter, Casey M.; Geraci, Gianluca; Schiavazzi, Daniele E.; Kahn, Andrew M.; Marsden, Alison L.: Multilevel and multifidelity uncertainty quantification for cardiovascular hemodynamics (2020)
  4. Girardi, Maria; Padovani, Cristina; Pellegrini, Daniele; Porcelli, Margherita; Robol, Leonardo: Finite element model updating for structural applications (2020)
  5. 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)
  6. Markus Frings, Norbert Hosters, Corinna Müller, Max Spahn, Christoph Susen, Konstantin Key, Stefanie Elgeti: SplineLib: A Modern Multi-Purpose C++ Spline Library (2020) arXiv
  7. Pinto, Jefferson Wellano Oliveira; Tueros, Juan Alberto Rojas; Horowitz, Bernardo; da Silva, Silvana Maria Bastos Afonso; Willmersdorf, Ramiro Brito; de Oliveira, Diego Felipe Barbosa: Gradient-free strategies to robust well control optimization (2020)
  8. Sauk, Benjamin; Ploskas, Nikolaos; Sahinidis, Nikolaos: GPU parameter tuning for tall and skinny dense linear least squares problems (2020)
  9. Tillmann Muhlpfordt, Frederik Zahn, Veit Hagenmeyer, Timm Faulwasser: PolyChaos.jl - A Julia Package for Polynomial Chaos in Systems and Control (2020) arXiv
  10. Vanslette, Kevin; Al Alsheikh, Abdullatif; Youcef-Toumi, Kamal: Why simple quadrature is just as good as Monte Carlo (2020)
  11. Zachary del Rosario: Grama: A Grammar of Model Analysis (2020) not zbMATH
  12. 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
  13. De Donno, Remo; Ghidoni, Antonio; Noventa, Gianmaria; Rebay, Stefano: Shape optimization of the ERCOFTAC centrifugal pump impeller using open-source software (2019)
  14. Gladish, Daniel W.; Darnell, Ross; Thorburn, Peter J.; Haldankar, Bhakti: Emulated multivariate global sensitivity analysis for complex computer models applied to agricultural simulators (2019)
  15. Katherine R. Barnhart, Eric Hutton, Gregory E. Tucker: umami: A Python package for Earth surface dynamics objective function construction (2019) not zbMATH
  16. Rezaeiravesh, S.; Mukha, T.; Liefvendahl, M.: Systematic study of accuracy of wall-modeled large eddy simulation using uncertainty quantification techniques (2019)
  17. Um, Kimoon; Hall, Eric J.; Katsoulakis, Markos A.; Tartakovsky, Daniel M.: Causality and Bayesian network PDEs for multiscale representations of porous media (2019)
  18. Wu, Xu; Shirvan, Koroush; Kozlowski, Tomasz: Demonstration of the relationship between sensitivity and identifiability for inverse uncertainty quantification (2019)
  19. Bergmann, Michel; Ferrero, Andrea; Iollo, Angelo; Lombardi, Edoardo; Scardigli, Angela; Telib, Haysam: A zonal Galerkin-free POD model for incompressible flows (2018)
  20. Butler, T.; Jakeman, J.; Wildey, T.: Convergence of probability densities using approximate models for forward and inverse problems in uncertainty quantification (2018)

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