Affiliations: [a] Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy | [b] Institute of Mechatronics and Information Systems, Lodz University of Technology, Lodz, Poland
Correspondence:
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Corresponding author: Maria Evelina Mognaschi, Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy. E-mail: [email protected]
Abstract: In computational electromagnetism there are manyfold advantages when using machine learning methods, because no mathematical formulation is required to solve the direct problem for given input geometry. Moreover, thanks to the inherent bidirectionality of a convolutional neural network, it can be trained to identify the geometry giving rise to the prescribed output field. All this puts the ground for the neural meta-modeling of fields, in spite of different levels of cost and accuracy. In the paper it is shown how CNNs can be trained to solve problems of optimal shape synthesis, with training data sets based on finite-element analyses of electric and magnetic fields. In particular, a concept of multi-fidelity model makes it possible to control both prediction accuracy and computational cost. The shape design of a MEMS design and the TEAM workshop problem 35 are considered as the case studies.
Keywords: Surrogate models, convolutional neural network, field analysis and synthesis
