Chaospy: An open source tool for designing methods of uncertainty quantification. The paper describes the philosophy, design, functionality, and usage of the Python software toolbox Chaospy for performing uncertainty quantification via polynomial chaos expansions and Monte Carlo simulation. The paper compares Chaospy to similar packages and demonstrates a stronger focus on defining reusable software building blocks that can easily be assembled to construct new, tailored algorithms for uncertainty quantification. For example, a Chaospy user can in a few lines of high-level computer code define custom distributions, polynomials, integration rules, sampling schemes, and statistical metrics for uncertainty analysis. In addition, the software introduces some novel methodological advances, like a framework for computing Rosenblatt transformations and a new approach for creating polynomial chaos expansions with dependent stochastic variables.

References in zbMATH (referenced in 10 articles )

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  1. Henkes, Alexander; Caylak, Ismail; Mahnken, Rolf: A deep learning driven pseudospectral PCE based FFT homogenization algorithm for complex microstructures (2021)
  2. Novák, Lukáš; Vořechovský, Miroslav; Sadílek, Václav; Shields, Michael D.: Variance-based adaptive sequential sampling for polynomial chaos expansion (2021)
  3. 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
  4. Tillmann Muhlpfordt, Frederik Zahn, Veit Hagenmeyer, Timm Faulwasser: PolyChaos.jl - A Julia Package for Polynomial Chaos in Systems and Control (2020) arXiv
  5. Wu, Jinhui; Zhang, Dequan; Liu, Jie; Jia, Xinyu; Han, Xu: A computational framework of kinematic accuracy reliability analysis for industrial robots (2020)
  6. Laloy, Eric; Jacques, Diederik: Emulation of CPU-demanding reactive transport models: a comparison of Gaussian processes, polynomial chaos expansion, and deep neural networks (2019)
  7. Lei, Huan; Li, Jing; Gao, Peiyuan; Stinis, Panagiotis; Baker, Nathan A.: A data-driven framework for sparsity-enhanced surrogates with arbitrary mutually dependent randomness (2019)
  8. Feinberg, Jonathan; Eck, Vinzenz Gregor; Langtangen, Hans Petter: Multivariate polynomial chaos expansions with dependent variables (2018)
  9. Hauseux, Paul; Hale, Jack S.; Cotin, Stéphane; Bordas, Stéphane P. A.: Quantifying the uncertainty in a hyperelastic soft tissue model with stochastic parameters (2018)
  10. Hauseux, Paul; Hale, Jack S.; Bordas, Stéphane P. A.: Accelerating Monte Carlo estimation with derivatives of high-level finite element models (2017)