Abstract: Technical solutions are described for performing sensitivity analysis for engineering systems in spatial-temporal domain. An example method includes receiving a set of process parameters and retrieving a multidimensional dataset containing historical values of the process parameters and corresponding output values. The method further includes selecting a sampling algorithm to divide the multidimensional dataset into multiple subspaces, and selecting multiple samples (xi), one sample from each subspace. The method further includes perturbing the samples, computing a first effect (EEi) on an output value (y), and computing a second effect (SEEii) of perturbing a pair of samples (xi and xj) on the output value. The method further includes computing a sensitivity coefficient of the process parameters on the output value using the second effect for xi and xj, the first effect for xi, and the first effect for xj. An automatic visualization scheme for the global sensitivity results is also provided.

Abstract: Technical solutions are described for performing sensitivity analysis for engineering systems in spatial-temporal domain. An example method includes receiving a set of process parameters and retrieving a multidimensional dataset containing historical values of the process parameters and corresponding output values. The method further includes selecting a sampling algorithm to divide the multidimensional dataset into multiple subspaces, and selecting multiple samples (xi), one sample from each subspace. The method further includes perturbing the samples, computing a first effect (EEi ) on an output value (y), and computing a second effect (SEEii) of perturbing a pair of samples (xi and xj) on the output value. The method further includes computing a sensitivity coefficient of the process parameters on the output value using the second effect for xi and xj, the first effect for xi, and the first effect for xj. An automatic visualization scheme for the global sensitivity results is also provided.