Uncertainsci: Uncertainty Quantification for Computational Models in Biomedicine and Bioengineering

Background Computational biomedical simulations frequently contain parameters that model physical features, material coeﬃcients, and physiological eﬀects, whose values are typically assumed known a priori . Understanding the eﬀect of variability in those assumed values is currently a topic of great interest. A general-purpose software tool that quantiﬁes how variation intheseparametersaﬀectsmodeloutputsisnotbroadlyavailableinbiomedicine. Forthisreason, we developed the ‘UncertainSCI’ uncertainty quantiﬁcation software suite to facilitate analysis of uncertainty due to parametric variability. Methods We developed and distributed a new open-source Python-based software tool, Un- certainSCI, which employs advanced parameter sampling techniques to build polynomial chaos (PC) emulators that can be used to predict model outputs for general parameter values. Un- certainty of model outputs is studied by modeling parameters as random variables, and model output statistics and sensitivities are then easily computed from the emulator. Our approaches utilize modern, near-optimal techniques for sampling and PC construction based on weighted Fekete points constructed by subsampling from a suitably randomized candidate set.