This article develops a data-driven magnetostatic finite-element (FE) solver that directly
exploits the measured material data instead of a material curve constructed from it. The …

Our ultimate goal is a topology optimization for a permanent-magnet (PM) machine, while
including material uncertainties. The uncertainties in the output data are, eg, due to …

In this paper, we focus on incorporating a stochastic collocation method (SCM) into a
topological shape optimization of a power semiconductor device, including material and …

This work addresses the magnetoquasistatic approximation of Maxwell's equations with
uncertainties in material data, shape and current sources, originating, eg, from …

This paper addresses the nonlinear elliptic curl--curl equation with uncertainties in the
material law. It is frequently employed in the numerical evaluation of magnetostatic fields …

An optimization technique based on the combination of optimization algorithm and
sensitivity analysis is discussed. The technique allows reducing the number of optimized …

The design of electrical machines and high-voltage devices requires the simulation of non-
linear low frequency electromagnetic problems in time domain. Typically magneto-and …

Purpose Manufacturing processes, such as laminations, may introduce uncertainties in the
magnetic properties of materials used in electrical machines. This issue, together with …

In this paper we focus on a shape/topology optimization problem of a power MOS transistor
under geometrical and material uncertainties to reduce the current density overshoot. This …

The aim of this paper is to incorporate the stochastic collocation method (SCM) into a
topology optimization for a permanent magnet (PM) synchronous machine with material …