pyVISCOUS: An open-source tool for computationally frugal global
sensitivity analysis
Abstract
Sensitivity analysis is used to increase our understanding of the
evaluated model and ease model parameter estimation. VISCOUS
(VarIance-based Sensitivity analysis using COpUlaS) is a given-data,
computationally frugal variance-based global sensitivity analysis
framework. Grounded in Copula theory, VISCOUS computes the Sobol
sensitivity indices using a probability model that describes the
relationship between model inputs (e.g., the perturbations in the model
parameters) and outputs (e.g., the model responses given a parameter
perturbation). In this technical note, we make three contributions to
make the VISCOUS framework easier to understand and apply. First, we
provide additional derivations of VISCOUS to connect the VISCOUS
framework to recent developments in the data science community. We
provide didactic examples with simple test functions in order to help a
wider group of modelers understand the underpinnings of the VISCOUS
framework. Second, we evaluate the VISCOUS framework using three types
of Sobol functions and provide a cautionary note on using VISCOUS to
approximate Sobol’ sensitivity indices for applications where model
inputs are of similar importance. Third, we provide an open-source code
of VISCOUS in Python, namely, pyVISCOUS. pyVISCOUS is model-independent
and can be applied with user-provided input-output data.