Data-driven sensitivity indices for models with dependent inputs using polynomial chaos expansions: Fid-Bau Portal

Data-driven sensitivity indices for models with dependent inputs using polynomial chaos expansions

Liu, Zhanlin / Choe, Youngjun

Highlights • New sensitivity indices are proposed for models with dependent inputs. • The sensitivity indices quantify how dependent inputs affect the output variance. • The sensitivity indices can be estimated using data of model inputs and outputs. • The sensitivity indices do not require any assumptions on model inputs. • The sensitivity indices are validated using four numerical examples.

Abstract Uncertainties exist in both physics-based and data-driven models. Variance-based sensitivity analysis characterizes how the variance of a model output is propagated from the model inputs. The Sobol index is one of the most widely used sensitivity indices for models with independent inputs. For models with dependent inputs, different approaches have been explored to obtain sensitivity indices in the literature. Typical approaches are based on procedures of transforming the dependent inputs into independent inputs. However, such transformation requires additional information about the inputs, such as the dependency structure or the conditional probability density functions. In this paper, data-driven sensitivity indices are proposed for models with dependent inputs. We first construct ordered partitions of linearly independent polynomials of the inputs. The modified Gram-Schmidt algorithm is then applied to the ordered partitions to generate orthogonal polynomials with respect to the empirical measure based on observed data of model inputs and outputs. Using the polynomial chaos expansion with the orthogonal polynomials, we obtain the proposed data-driven sensitivity indices. The sensitivity indices provide intuitive interpretations of how the dependent inputs affect the variance of the output without a priori knowledge on the dependence structure of the inputs. Four numerical examples are used to validate the proposed approach.