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Magnetic material properties of an electromagnetic device (EMD) can be recovered by solving a coupled experimental numerical inverse problem. In order to ensure the highest possible accuracy of the inverse problem solution, all physics of the EMD need to be perfectly modeled using a complex numerical model. However, these fine models demand a high computational time. Alternatively, less accurate coarse models can be used with a demerit of the high expected recovery errors. The purpose of this paper is to present an efficient methodology to reduce the effect of stochastic modeling errors in the inverse problem solution.

The recovery error in the electromagnetic inverse problem solution is reduced using the Bayesian approximation error approach coupled with an adaptive Kriging-based model. The accuracy of the forward model is assessed and adapted a priori using the cross-validation technique.

The adaptive Kriging-based model seems to be an efficient technique for modeling EMDs used in inverse problems. Moreover, using the proposed methodology, the recovery error in the electromagnetic inverse problem solution is largely reduced in a relatively small computational time and memory storage.

The proposed methodology is capable of not only improving the accuracy of the inverse problem solution, but also reducing the computational time as well as the memory storage. Furthermore, to the best of the authors knowledge, it is the first time to combine the adaptive Kriging-based model with the Bayesian approximation error approach for the stochastic modeling error reduction.

The purpose of this paper is to determine a priori the optimal needle placement so to achieve an as accurate as possible magnetic property identification of an electromagnetic device. Moreover, the effect of the uncertainties in the geometrical parameter values onto the optimal sensor position is studied.

The optimal needle placement is determined using the stochastic Cramér‐Rao lower bound method. The results obtained using the stochastic method are compared with a first order sensitivity analysis. The inverse problem is solved starting from real local magnetic induction measurements coupled with a 3D finite element model of an electromagnetic device (EI core inductor).

The optimal experimental design for the identification of the magnetic properties of an electromagnetic device is achieved. The uncertainties in the geometrical model parameters have a high effect on the inverse problem recovered solution.

The solution of the inverse problem is more accurate because the measurements are carried out at the optimal positions, in which the effects of the uncertainties in the geometrical model parameters are limited.