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 77 articles )

Showing results 1 to 20 of 77.
Sorted by year (citations)

1 2 3 4 next

  1. Martin, Sergio M.; Wälchli, Daniel; Arampatzis, Georgios; Economides, Athena E.; Karnakov, Petr; Koumoutsakos, Petros: Korali: efficient and scalable software framework for Bayesian uncertainty quantification and stochastic optimization (2022)
  2. Ploskas, Nikolaos; Sahinidis, Nikolaos V.: Review and comparison of algorithms and software for mixed-integer derivative-free optimization (2022)
  3. Birmpa, Panagiota; Katsoulakis, Markos A.: Uncertainty quantification for Markov random fields (2021)
  4. Brigitte Boden, Jan Flink, Niklas Först, Robert Mischke, Kathrin Schaffert, Alexander Weinert, Annika Wohlan, Andreas Schreiber: RCE: An Integration Environment for Engineering and Science (2021) not zbMATH
  5. Eason, John P.; Biegler, Lorenz T.: Model reduction in chemical process optimization (2021)
  6. Ghantasala, Aditya; Najian Asl, Reza; Geiser, Armin; Brodie, Andrew; Papoutsis, Efthymios; Bletzinger, Kai-Uwe: Realization of a framework for simulation-based large-scale shape optimization using vertex morphing (2021)
  7. Tosi, Riccardo; Amela, Ramon; Badia, Rosa M.; Rossi, Riccardo: A parallel dynamic asynchronous framework for uncertainty quantification by hierarchical Monte Carlo algorithms (2021)
  8. Villa, Umberto; Petra, Noemi; Ghattas, Omar: hIPPYlib. An extensible software framework for large-scale inverse problems governed by PDEs. I: Deterministic inversion and linearized Bayesian inference (2021)
  9. 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)
  10. Degen, Denise; Veroy, Karen; Wellmann, Florian: Certified reduced basis method in geosciences. Addressing the challenge of high-dimensional problems (2020)
  11. Fleeter, Casey M.; Geraci, Gianluca; Schiavazzi, Daniele E.; Kahn, Andrew M.; Marsden, Alison L.: Multilevel and multifidelity uncertainty quantification for cardiovascular hemodynamics (2020)
  12. Girardi, Maria; Padovani, Cristina; Pellegrini, Daniele; Porcelli, Margherita; Robol, Leonardo: Finite element model updating for structural applications (2020)
  13. 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)
  14. Markus Frings, Norbert Hosters, Corinna Müller, Max Spahn, Christoph Susen, Konstantin Key, Stefanie Elgeti: SplineLib: A Modern Multi-Purpose C++ Spline Library (2020) arXiv
  15. 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)
  16. Sauk, Benjamin; Ploskas, Nikolaos; Sahinidis, Nikolaos: GPU parameter tuning for tall and skinny dense linear least squares problems (2020)
  17. Tillmann Muhlpfordt, Frederik Zahn, Veit Hagenmeyer, Timm Faulwasser: PolyChaos.jl - A Julia Package for Polynomial Chaos in Systems and Control (2020) arXiv
  18. Vanslette, Kevin; Al Alsheikh, Abdullatif; Youcef-Toumi, Kamal: Why simple quadrature is just as good as Monte Carlo (2020)
  19. Zachary del Rosario: Grama: A Grammar of Model Analysis (2020) not zbMATH
  20. 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

1 2 3 4 next


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