UQLab

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 21 to 40 of 43.
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  1. Tosin, Michel; Côrtes, Adriano M. A.; Cunha, Americo: A tutorial on Sobol’ global sensitivity analysis applied to biological models (2020)
  2. Weise K., Poßner L., Müller E., Gast R., Knösche T.R.: Pygpc: A sensitivity and uncertainty analysis toolbox for Python (2020) not zbMATH
  3. Zachary del Rosario: Grama: A Grammar of Model Analysis (2020) not zbMATH
  4. 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)
  5. Fenzi, Luca; Michiels, Wim: Polynomial (chaos) approximation of maximum eigenvalue functions. Efficiency and limitations (2019)
  6. Hart, J. L.; Gremaud, P. A.; David, T.: Global sensitivity analysis of high-dimensional neuroscience models: an example of neurovascular coupling (2019)
  7. Khazaie, Shahram; Wang, Xun; Komatitsch, Dimitri; Sagaut, Pierre: Uncertainty quantification for acoustic wave propagation in a shallow water environment (2019)
  8. Naik, Pratik; Pandita, Piyush; Aramideh, Soroush; Bilionis, Ilias; Ardekani, Arezoo M.: Bayesian model calibration and optimization of surfactant-polymer flooding (2019)
  9. Sauder, Thomas; Marelli, Stefano; Sørensen, Asgeir J.: Probabilistic robust design of control systems for high-fidelity cyber-physical testing (2019)
  10. Shahane, Shantanu; Aluru, Narayana; Ferreira, Placid; Kapoor, Shiv G.; Vanka, Surya Pratap: Finite volume simulation framework for die casting with uncertainty quantification (2019)
  11. Vohra, Manav; Alexanderian, Alen; Safta, Cosmin; Mahadevan, Sankaran: Sensitivity-driven adaptive construction of reduced-space surrogates (2019)
  12. Zhou, Yicheng; Lu, Zhenzhou; Cheng, Kai; Ling, Chunyan: An efficient and robust adaptive sampling method for polynomial chaos expansion in sparse Bayesian learning framework (2019)
  13. Kasia Sawicka, Gerard B.M. Heuvelink, Dennis J.J. Walvoort: Spatial Uncertainty Propagation Analysis with the spup R Package (2018) not zbMATH
  14. Martínez-Frutos, Jesús; Periago Esparza, Francisco: Optimal control of PDEs under uncertainty. An introduction with application to optimal shape design of structures (2018)
  15. Vahedi, Jafar; Ghasemi, Mohammad Reza; Miri, Mahmoud: An adaptive divergence-based method for structural reliability analysis via multiple kriging models (2018)
  16. Abraham, Simon; Raisee, Mehrdad; Ghorbaniasl, Ghader; Contino, Francesco; Lacor, Chris: A robust and efficient stepwise regression method for building sparse polynomial chaos expansions (2017)
  17. Ahmed Attia, Adrian Sandu: DATeS: A Highly-Extensible Data Assimilation Testing Suite (2017) arXiv
  18. Fajraoui, Noura; Marelli, Stefano; Sudret, Bruno: Sequential design of experiment for sparse polynomial chaos expansions (2017)
  19. Hamdi, Hamidreza; Couckuyt, Ivo; Sousa, Mario Costa; Dhaene, Tom: Gaussian processes for history-matching: application to an unconventional gas reservoir (2017)
  20. Mai, Chu V.; Sudret, Bruno: Surrogate models for oscillatory systems using sparse polynomial chaos expansions and stochastic time warping (2017)