UQLab: The Framework for Uncertainty Quantification. UQLab is a Matlab-based software framework designed to bring state-of-the art uncertainty quantification (UQ) techniques and algorithms to a large audience. UQLab is not simply an umpteenth toolbox for UQ, but a framework: not only it offers you an extensive arsenal of built-in types of analyses and algorithms but it also provides a powerful new way of developing and implementing your own ideas. The project originated in 2013, when Prof. Bruno Sudret founded the Chair of Risk, Safety and Uncertainty Quantification at ETH Zurich, and decided to gather the results of a decade of his research into a single software tool. UQLab provides now the software backbone of the Chair’s research, allowing for seamless integration between the many research fields engaged by its members, e.g. metamodeling (polynomial chaos expansions, Gaussian process modelling, a.k.a. Kriging, low-rank tensor approximations), rare event estimation (structural reliability), global sensitivity analysis, Bayesian techniques for inverse problems, etc. After more than two years of development it was decided to open the platform to other research institutions, in an effort to increase the awareness of the scientific community regarding the fundamental aspects of uncertainty quantification. The first closed beta version is online since July 1st, 2015.

References in zbMATH (referenced in 43 articles )

Showing results 1 to 20 of 43.
Sorted by year (citations)

1 2 3 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. N., Navaneeth; Chakraborty, Souvik: Surrogate assisted active subspace and active subspace assisted surrogate -- a new paradigm for high dimensional structural reliability analysis (2022)
  3. Rossat, D.; Baroth, J.; Briffaut, M.; Dufour, F.: Bayesian inversion using adaptive polynomial chaos kriging within subset simulation (2022)
  4. Song, Chaolin; Wang, Zeyu; Shafieezadeh, Abdollah; Xiao, Rucheng: BUAK-AIS: efficient Bayesian updating with active learning kriging-based adaptive importance sampling (2022)
  5. Lüthen, Nora; Marelli, Stefano; Sudret, Bruno: Sparse polynomial chaos expansions: literature survey and benchmark (2021)
  6. Man, Jun; Lin, Guang; Yao, Yijun; Zeng, Lingzao: A generalized multi-fidelity simulation method using sparse polynomial chaos expansion (2021)
  7. Novák, Lukáš; Vořechovský, Miroslav; Sadílek, Václav; Shields, Michael D.: Variance-based adaptive sequential sampling for polynomial chaos expansion (2021)
  8. Sun, Xiang; Pan, Xiaomin; Choi, Jung-Il: Non-intrusive framework of reduced-order modeling based on proper orthogonal decomposition and polynomial chaos expansion (2021)
  9. Wagner, Paul-Remo; Marelli, Stefano; Sudret, Bruno: Bayesian model inversion using stochastic spectral embedding (2021)
  10. Wang, Jinsheng; Li, Chenfeng; Xu, Guoji; Li, Yongle; Kareem, Ahsan: Efficient structural reliability analysis based on adaptive Bayesian support vector regression (2021)
  11. Zhang, Yu; Xu, Jun: Efficient reliability analysis with a CDA-based dimension-reduction model and polynomial chaos expansion (2021)
  12. Zhu, Xujia; Sudret, Bruno: Emulation of stochastic simulators using generalized lambda models (2021)
  13. Bhattacharyya, Biswarup: Global sensitivity analysis: a Bayesian learning based polynomial chaos approach (2020)
  14. Felix Petzke, Ali Mesbah, Stefan Streif: PoCET: a Polynomial Chaos Expansion Toolbox for Matlab (2020) arXiv
  15. Florian, Francesco; Vermiglio, Rossana: PC-based sensitivity analysis of the basic reproduction number of population and epidemic models (2020)
  16. Lu, Xuefei; Rudi, Alessandro; Borgonovo, Emanuele; Rosasco, Lorenzo: Faster Kriging: facing high-dimensional simulators (2020)
  17. Robin A. Richardson, David W. Wright, Wouter Edeling, Vytautas Jancauskas, Jalal Lakhlili, Peter V. Coveney: EasyVVUQ: A Library for Verification, Validation and Uncertainty Quantification in High Performance Computing (2020) not zbMATH
  18. Sun, Xiang; Choi, Yun Young; Choi, Jung-Il: Global sensitivity analysis for multivariate outputs using polynomial chaos-based surrogate models (2020)
  19. Thapa, Mishal; Mulani, Sameer B.; Walters, Robert W.: Adaptive weighted least-squares polynomial chaos expansion with basis adaptivity and sequential adaptive sampling (2020)
  20. Tillmann Muhlpfordt, Frederik Zahn, Veit Hagenmeyer, Timm Faulwasser: PolyChaos.jl - A Julia Package for Polynomial Chaos in Systems and Control (2020) arXiv

1 2 3 next