Abstract
We attempt to estimate the spatial distribution of heterogeneous physical parameters involved in the formation of magnetic domain patterns of polycrystalline thin films by using convolutional neural networks. We propose a method to obtain a spatial map of physical parameters by estimating the parameters from patterns within a small subregion window of the full magnetic domain and subsequently shifting this window. To enhance the accuracy of parameter estimation in such subregions, we employ employ large-scale models utilized for natural image classification and exploit the benefits of pretraining. Using a model with high estimation accuracy on these subregions, we conduct inference on simulation data featuring spatially varying parameters and demonstrate the capability to detect such parameter variations.
Abstract (translated)
我们用卷积神经网络尝试估算多晶片薄板磁 domains patterns 中涉及的不同物质参数的空间分布。我们提出了一种方法，通过从整个磁 domains 的小区域窗口中估计参数，然后移动这个窗口来得到物质参数的空间地图。为了提高在这些子区域中的参数估计精度，我们使用了用于自然图像分类的大型模型，并利用预训练的优势。使用在这些子区域中具有高精度估计模型的模型，我们对模拟数据进行了推断，其中包含空间分布参数的变化，并展示了能够检测这种参数变化的能力。
URL
https://arxiv.org/abs/2305.14764
