OpenTURNS: A Python package for uncertainty quantification

Michaël Baudin; Anne Dutfoy; Régis Lebrun; Julien Schueller; Sofiane Haddad; Loïc Brevault; Mathieu Balesdent; Julien Pelamatti; Joseph Muré

doi:10.21105/joss.10484

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Authors

Michaël Baudin (0000-0002-0450-7947), Anne Dutfoy (0000-0002-5139-106X), Régis Lebrun (0000-0003-3089-4642), Julien Schueller (0009-0006-2452-7861), Sofiane Haddad (0009-0006-5573-2534), Loïc Brevault (0000-0001-8081-6082), Mathieu Balesdent (0000-0003-4064-3361), Julien Pelamatti (0000-0002-1769-2715), Joseph Muré (0000-0003-4147-1143)

Citation

Baudin et al., (2026). OpenTURNS: A Python package for uncertainty quantification. Journal of Open Source Software, 11(125), 10484, https://doi.org/10.21105/joss.10484

@article{Baudin2026, doi = {10.21105/joss.10484}, url = {https://doi.org/10.21105/joss.10484}, year = {2026}, publisher = {The Open Journal}, volume = {11}, number = {125}, pages = {10484}, author = {Baudin, Michaël and Dutfoy, Anne and Lebrun, Régis and Schueller, Julien and Haddad, Sofiane and Brevault, Loïc and Balesdent, Mathieu and Pelamatti, Julien and Muré, Joseph}, title = {OpenTURNS: A Python package for uncertainty quantification}, journal = {Journal of Open Source Software} }

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Python uncertainty quantification probabilistic modeling statistics meta-modeling calibration inverse problems stochastic processes Bayesian modeling machine learning supervised machine learning unsupervised machine learning surrogate modeling polynomial chaos expansion gaussian process regression sensitivity analysis regression function approximation Quasi-Monte Carlo methods optimization reliability analysis Markov chain Monte Carlo copula dimensionality reduction maximum likelihood estimation importance sampling

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