UncertainSCI: A Python Package for Noninvasive Parametric Uncertainty Quantification of Simulation Pipelines

Jess Tate; Zexin Liu; Jake A. Bergquist; Sumientra Rampersad; Dan White; Chantel Charlebois; Lindsay Rupp; Dana H. Brooks; Rob S. MacLeod; Akil Narayan

doi:10.21105/joss.04249

Review

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Authors

Jess Tate (0000-0002-2934-1453), Zexin Liu (0000-0003-3409-5709), Jake A. Bergquist (0000-0002-4586-6911), Sumientra Rampersad (0000-0001-9860-4459), Dan White, Chantel Charlebois (0000-0002-4139-3539), Lindsay Rupp (0000-0002-2688-7688), Dana H. Brooks (0000-0003-3231-6715), Rob S. MacLeod (0000-0002-0000-0356), Akil Narayan (0000-0002-5914-4207)

Citation

Tate et al., (2023). UncertainSCI: A Python Package for Noninvasive Parametric Uncertainty Quantification of Simulation Pipelines. Journal of Open Source Software, 8(90), 4249, https://doi.org/10.21105/joss.04249

@article{Tate2023, doi = {10.21105/joss.04249}, url = {https://doi.org/10.21105/joss.04249}, year = {2023}, publisher = {The Open Journal}, volume = {8}, number = {90}, pages = {4249}, author = {Jess Tate and Zexin Liu and Jake A. Bergquist and Sumientra Rampersad and Dan White and Chantel Charlebois and Lindsay Rupp and Dana H. Brooks and Rob S. MacLeod and Akil Narayan}, title = {UncertainSCI: A Python Package for Noninvasive Parametric Uncertainty Quantification of Simulation Pipelines}, journal = {Journal of Open Source Software} }

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