论文标题
使用深度学习探索纳米晶永久磁铁的滞后特性
Exploring the hysteresis properties of nanocrystalline permanent magnets using deep learning
论文作者
论文摘要
我们证明了使用模型阶的减少和神经网络来估计微结构中纳米晶永久磁体的磁滞性能。通过数据驱动的方法，我们从晶粒生长和微磁模拟创建的数据集中学习了消灭曲线。我们表明，磁铁的颗粒结构可以在低维的潜在空间中编码。潜在代码是使用变异自动编码器构建的。结构代码对磁滞性属性的映射是一个多目标回归问题。我们应用深度神经网络并使用参数共享，以便从磁铁的结构代码中预测沿电磁曲线的锚点。该方法用于研究纳米晶永久磁体的磁性。我们展示了如何通过潜在空间中两个点之间的两个点之间的插值以及如何预测所得磁体的磁性。
We demonstrate the use of model order reduction and neural networks for estimating the hysteresis properties of nanocrystalline permanent magnets from microstructure. With a data-driven approach, we learn the demagnetization curve from data-sets created by grain growth and micromagnetic simulations. We show that the granular structure of a magnet can be encoded within a low-dimensional latent space. Latent codes are constructed using a variational autoencoder. The mapping of structure code to hysteresis properties is a multi-target regression problem. We apply deep neural network and use parameter sharing, in order to predict anchor points along the demagnetization curves from the magnet's structure code. The method is applied to study the magnetic properties of nanocrystalline permanent magnets. We show how new grain structures can be generated by interpolation between two points in the latent space and how the magnetic properties of the resulting magnets can be predicted.
