Uncertainty Quantification Using Sparse Approximation for Models With a High Number of Parameters: Application to a Magnetoelectric Sensor

To face the curse of dimensionality met in uncertainty quantification problems when the model has a high number of random parameters, methods based on sparse approximation, such as the least angle regression (LAR) method, should be used. In this paper, we propose an extension of the LAR method, and we apply it to quantify the impact of uncertainties on the characteristics of the material on the magnetoelectric sensor performance. The sensor response is represented by a 2-D finite-element model with ten random parameters. A global sensitivity analysis is carried out in order to determine the most influential parameters.