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Optimization of electromagnetic devices: circuit models, neural networks and gradient methods in concert

机译：电磁设备的优化：电路模型，神经网络和梯度方法协调一致

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摘要

Optimization in designing electromagnetic products is now increasingly better understood. As opposed to classical models of magnetic circuits, today, gradient techniques for mathematical optimization have been proposed and are used. These techniques, while being expensive, are exact. More recently, artificial neural networks have been suggested, but they, work best only if the data set of parameter-set, performance pairs for training the network is close to the optimal solution that we seek. In this paper, it is shown how all three methods may be used in concert to increase efficiency. The circuit model is used to generate an approximate inverse solution. Then direct finite element solutions are used to generate the required training set and this is used with the neural network to get a better solution. This solution is finally used as a starting point for the gradient optimization scheme which converges quickly because the starting point is close to the actual solution.
机译：现在，人们对电磁产品设计的优化越来越了解。与经典的磁路模型相反，如今，已经提出并使用了用于数学优化的梯度技术。这些技术虽然昂贵，但却是精确的。最近，有人提出了人工神经网络，但只有在训练网络的参数集，性能对的数据集接近我们寻求的最佳解决方案时，它们才能发挥最佳作用。在本文中，显示了如何同时使用这三种方法来提高效率。电路模型用于生成近似逆解。然后将直接有限元解用于生成所需的训练集，并将其与神经网络一起使用以获得更好的解。最终，该解决方案被用作梯度优化方案的起点，该梯度收敛方案很快收敛，因为该起点接近实际解决方案。

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