A neural inverse problem approach for optimal design

Abstract

An original approach to the optimization of electromagnetic structures is presented that makes use of a neural network trained to capture the functional relationship between the design parameters and the objective function. The algebraic structure of the network is used to find the basins of attraction of the objective function of the optimization problem, avoiding the major drawbacks of the commonly used algorithms, i.e., the entrapment in local minima, and/or the huge amount of cost function evaluations.