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 34 articles )

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  1. Lüthen, Nora; Marelli, Stefano; Sudret, Bruno: Sparse polynomial chaos expansions: literature survey and benchmark (2021)
  2. Man, Jun; Lin, Guang; Yao, Yijun; Zeng, Lingzao: A generalized multi-fidelity simulation method using sparse polynomial chaos expansion (2021)
  3. Sun, Xiang; Pan, Xiaomin; Choi, Jung-Il: Non-intrusive framework of reduced-order modeling based on proper orthogonal decomposition and polynomial chaos expansion (2021)
  4. Zhang, Yu; Xu, Jun: Efficient reliability analysis with a CDA-based dimension-reduction model and polynomial chaos expansion (2021)
  5. Bhattacharyya, Biswarup: Global sensitivity analysis: a Bayesian learning based polynomial chaos approach (2020)
  6. Felix Petzke, Ali Mesbah, Stefan Streif: PoCET: a Polynomial Chaos Expansion Toolbox for Matlab (2020) arXiv
  7. Florian, Francesco; Vermiglio, Rossana: PC-based sensitivity analysis of the basic reproduction number of population and epidemic models (2020)
  8. Lu, Xuefei; Rudi, Alessandro; Borgonovo, Emanuele; Rosasco, Lorenzo: Faster Kriging: facing high-dimensional simulators (2020)
  9. 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
  10. Sun, Xiang; Choi, Yun Young; Choi, Jung-Il: Global sensitivity analysis for multivariate outputs using polynomial chaos-based surrogate models (2020)
  11. Thapa, Mishal; Mulani, Sameer B.; Walters, Robert W.: Adaptive weighted least-squares polynomial chaos expansion with basis adaptivity and sequential adaptive sampling (2020)
  12. Tillmann Muhlpfordt, Frederik Zahn, Veit Hagenmeyer, Timm Faulwasser: PolyChaos.jl - A Julia Package for Polynomial Chaos in Systems and Control (2020) arXiv
  13. Tosin, Michel; Côrtes, Adriano M. A.; Cunha, Americo: A tutorial on Sobol’ global sensitivity analysis applied to biological models (2020)
  14. Zachary del Rosario: Grama: A Grammar of Model Analysis (2020) not zbMATH
  15. Zhou, Yicheng; Lu, Zhenzhou; Hu, Jinghan; Hu, Yingshi: Surrogate modeling of high-dimensional problems via data-driven polynomial chaos expansions and sparse partial least square (2020)
  16. Fenzi, Luca; Michiels, Wim: Polynomial (chaos) approximation of maximum eigenvalue functions. Efficiency and limitations (2019)
  17. Hart, J. L.; Gremaud, P. A.; David, T.: Global sensitivity analysis of high-dimensional neuroscience models: an example of neurovascular coupling (2019)
  18. Khazaie, Shahram; Wang, Xun; Komatitsch, Dimitri; Sagaut, Pierre: Uncertainty quantification for acoustic wave propagation in a shallow water environment (2019)
  19. Naik, Pratik; Pandita, Piyush; Aramideh, Soroush; Bilionis, Ilias; Ardekani, Arezoo M.: Bayesian model calibration and optimization of surfactant-polymer flooding (2019)
  20. Sauder, Thomas; Marelli, Stefano; Sørensen, Asgeir J.: Probabilistic robust design of control systems for high-fidelity cyber-physical testing (2019)

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