1,720,992 research outputs found
A non-differential method for solving many-objective optimization problems: An application in IPM motor design
No full textMany-objective problems, characterized by a high-dimensional objective space, are a source of challenge for standard methods of multi-objective optimization. An evolutionary algorithm based on the concept of least-conflict solutions is here presented; the shape design of a IPM motor is considered as the case study. In particular, optimization problems dealing with 3 and 4 objective functions have been solved, respectively
CNN-Based Surrogate Models of the Electrostatic Field for a MEMS Motor: A Bi-Objective Optimal Shape Design
No full textThe use of a convolutional neural network to develop a surrogate model of the electric field in MEMS devices is proposed. An electrostatic micromotor is considered as the case study. In particular, different CNNs are trained for the prediction of the torque profile and the maximum torque value at a no-load condition and the radial force which could arise in case of the radial displacement of the rotor during motion. The proposed deep learning approach is able to predict the abovementioned quantities with a low error and, in particular, it allows for a decrease in the computational cost, especially in case of optimization problems based on FE models
Neural metamodelling of fields: Towards a new deal in computational electromagnetics
Get PDFIn 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
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