Welcome to UncertainSCI’s documentation!


UncertainSCI source code

About UncertainSCI

UncertainSCI [1] is a Python-based toolkit that harnesses modern techniques to estimate model and parametric uncertainty, with a particular emphasis on needs for biomedical simulations and applications. This toolkit enables non-intrusive integration of these techniques with well-established biomedical simulation software.

Currently implemented in UncertainSCI is Polynomial Chaos Expansion (PCE) with a number of input distributions. For more information about these techniques, see: [2, 3, 4, 5, 6]. For studies using UncertainSCI, see: [7, 8, 9, 10, 11, 12]


Jake Bergquist, Dana Brooks, Zexin Liu, Rob MacLeod, Akil Narayan, Sumientra Rampersad, Lindsay Rupp, Jess Tate, Dan White


This project was supported by grants from the National Institute of Biomedical Imaging and Bioengineering (U24EB029012) from the National Institutes of Health.

Indices and Tables



Akil Narayan, Zexin Liu, Jake Bergquist, Chantel Charlebois, Sumientra Rampersad, Lindsay Rupp, Dana Brooks, Dan White, Jess Tate, and Rob S MacLeod. UncertainSCI: uncertainty quantification for computational models in biomedicine and bioengineering. Available at SSRN 4049696, 2022.


Kyle M. Burk, Akil Narayan, and Joseph A. Orr. Efficient sampling for polynomial chaos-based uncertainty quantification and sensitivity analysis using weighted approximate fekete points. International Journal for Numerical Methods in Biomedical Engineering, 36(11):e3395, 2020. doi:https://doi.org/10.1002/cnm.3395.


Akil Narayan. Computation of induced orthogonal polynomial distributions. Electronic Transactions on Numerical Analysis, 50:71–97, 2018. arXiv:1704.08465 [math]. URL: https://epub.oeaw.ac.at?arp=0x003a184e, doi:10.1553/etna_vol50s71.


L. Guo, A. Narayan, L. Yan, and T. Zhou. Weighted Approximate Fekete Points: Sampling for Least-Squares Polynomial Approximation. SIAM Journal on Scientific Computing, 40(1):A366–A387, 2018. arXiv:1708.01296 [math.NA]. URL: http://epubs.siam.org/doi/abs/10.1137/17M1140960, doi:10.1137/17M1140960.


Albert Cohen and Giovanni Migliorati. Optimal weighted least-squares methods. SMAI Journal of Computational Mathematics, 3:181–203, 2017. arxiv:1608.00512 [math.NA]. doi:10.5802/smai-jcm.24.


S.K. Gupta and W.V. Harper. Sensitivity/Uncertainty Analysis of a Borehole Scenario Comparing Latin Hypercube Sampling and Deterministic Sensitivity Approaches. Technical Report BMI/ONWI-516, Battelle Memorial Institute, Office of Nuclear Waste Isolation, 1983.


Jake Bergquist, Brian Zenger, Lindsay Rupp, Akil Narayan, Jess Tate, and Rob MacLeod. Uncertainty quantification in simulations of myocardial ischemia. In Computing in Cardiology, volume 48. September 2021.


Lindsay C Rupp, Zexin Liu, Jake A Bergquist, Sumientra Rampersad, Dan White, Jess D Tate, Dana H. Brooks, Akil Narayan, and Rob S. MacLeod. Using uncertainSCI to quantify uncertainty in cardiac simulations. In Computing in Cardiology, volume 47. September 2020.


Lindsay C Rupp, Jake A Bergquist, Brian Zenger, Karli Gillette, Akil Narayan, Jess Tate, Gernot Plank, and Rob S. MacLeod. The role of myocardial fiber direction in epicardial activation patterns via uncertainty quantification. In Computing in Cardiology, volume 48. September 2021.


Jess D. Tate, Wilson W. Good, Nejib Zemzemi, Machteld Boonstra, Peter van Dam, Dana H. Brooks, Akil Narayan, and Rob S. MacLeod. Uncertainty quantification of the effects of segmentation variability in ECGI. In Functional Imaging and Modeling of the Heart, pages 515–522. Springer-Cham, Palo Alto, USA, 2021. doi:https://doi.org/10.1007/978-3-030-78710-3_49.


Jess Tate, Sumientra Rampersad, Chantel Charlebois, Zexin Liu, Jake Bergquist, Dan White, Lindsay Rupp, Dana Brooks, Akil Narayan, and Rob MacLeod. Uncertainty quantification in brain stimulation using uncertainSCI. Brain Stimulation: Basic, Translational, and Clinical Research in Neuromodulation, 14(6):1659–1660, January 2021. URL: https://doi.org/10.1016/j.brs.2021.10.226, doi:10.1016/j.brs.2021.10.226.


Jess D Tate, Nejib Zemzemi, Shireen Elhabian, Beáta Ondru\u sová, Machteld Boonstra, Peter van Dam, Akil Narayan, Dana H Brooks, and Rob S MacLeod. Segmentation uncertainty quantification in cardiac propagation models. In 2022 Computing in Cardiology (CinC), volume 498, 1–4. 2022. doi:10.22489/CinC.2022.419.