Deep Learning and Reduced Models for Fast Optimization in Electromagnetics

The computational cost of topology optimization based on the binary particle swarm optimization is greatly reduced by the use of deep neural networks (DNNs). A first convolutional neural network (CNN) is trained with data coming from finite-element analysis (FEA) with the aim of correctly estimating the output quantity (a motor torque in the proposed case study). A second CNN is trained to give as output the boundary conditions (BCs), expressed in terms of fields or potentials to be used as BC of a reduced finite-element method (FEM) model, created in order to still be able to give the correct value of the output quantity. In the optimization phase, the torque properties are evaluated by the trained CNN, and only a reduced percentage of cases are reevaluated by either the full FEM model or the reduced FEM model. The overall computational time of the optimization procedure is significantly reduced.