METHODS FOR PARAMETER RANKING IN NONLINEAR, MECHANISTIC MODELS

The paper addresses efficient methods for parameter sensitivity analysis and ranking in large, nonlinear, mechanistic models requiring examination of many points in the parameter space. The paper shows how orthogonal decomposition and permutation of the sensitivity derivative is an intuitive and structured method for automatic ranking of the parameters within a candidate set. Provided the model error is Gaussian, and with the problem on a triangularized form, the additional variance associated with each parameter can easily be found. Ranking according to additional variance is therefore another option. The methods are tested on an industrially used simulator model.