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A Neural Networks Inversion-Based Algorithm for Multiobjective Design of a High-Field Superconducting Dipole Magnet

In this paper, an original algorithm to solve multiobjective design problems is presented, which makes use of a neural network inversion method. The proposed approach makes possible to explore the solutions directly in the objectives space, with a great saving of computation time in the reconstruction of the Pareto front. An MLP neural network is firstly trained to solve the analysis design problem. The inversion of the neural model allows us to obtain the design parameters, starting from the desired requirements on all the conflicting multiple objectives. The performance of the method is demonstrated by application to the design of a high-field superconducting dipole magnet, where a trade-off between the superconductors and the iron volumes is required, in order to obtain a prescribed magnetic field value in the dipole axis