| Abstract: | This study proposes neural network‐based iterative inverse solutions for non‐destructive evaluation (NDE) in which vector finite elements (VFEM) represent the forward model that closely models the physical process. The iterative algorithm can eventually estimate the material parameters. Vector finite element method global matrix is stored in a compact form using its sparsity and symmetry. The stored matrix elements are employed as the neurons weights, and preconditioning techniques are used to accelerate convergence of the neural networks (NN) algorithm. Detailed algorithm describing this new method is given to facilitate implementation. Combining vector finite elements and NNs offers several advantages over each technique alone, such as reducing memory storage requirements and the easily computed fixed weights of the NN. Various examples are solved to show the performance and usefulness of the proposed method, including lossy printed circuit board and lossy inhomogeneous cylindrical problems with ferromagnetic materials. These solutions compare very well with other published data where the maximum relative error was 5%. Copyright © 2010 John Wiley & Sons, Ltd. |
