Model Order Reduction of Non-Linear Magnetostatic Problems Based on POD and DEI Methods

In the domain of numerical computation, model order reduction approaches are more and more frequently applied in mechanics and have shown their efficiency in terms of reduction of computation time and memory storage requirements. One of these approaches, the proper orthogonal decomposition (POD), can be very efficient in solving linear problems but encounters limitations in the non-linear (NL) case. In this paper, the discrete empirical interpolation method coupled with the POD method is presented. This is an interesting alternative to reduce large-scale systems deriving from the discretization of NL magnetostatic problems coupled with an external electrical circuit.