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A practical method of global sensitivity analysis under constraints

A novel practical method to conduct a Global Sensitivity Analysis (GSA) for computer models is proposed in this article. It is based on Partial Least Squares polynomial regression metamodeling, used in conjunction with a D-optimal computer experiment design. This practical method is particularly well adapted to the following four situations: (i) when the cost of a single computer simulation run is very high (long computing time), limiting computation to just hundreds of simulation runs, whereas the usual GSA methods (typically, those based on Latin Hypercube Samplings or Sobol sequences) require thousands or tens of thousands of simulation runs; (ii) when the inputs are not independent because some of them are stochastically linked (correlated) or deterministically (functionally) linked; (iii) when some inputs are of a qualitative (categorical) nature; and (iv) when the outputs are multivariate. This practical method, proposed in a deterministic simulation framework, is useful for moderately nonlinear computer models. It makes it possible to define and compute novel sensitivity indices, referred to here under the general term of SIVIP, not to be confused with Sobol indices defined for independent inputs. We have called our new method the SIVIP method. It is applied here to the aeronautics field, in particular, for the estimation of detection performances of infrared sensors and, more precisely, for the computation of aircraft InfraRed Signatures (IRS).