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.

通过卷积神经网络技术，对多晶薄膜磁畴图案形成中涉及的异质物理参数的空间分布进行了估计，并且利用大规模自然图像分类模型的预训练技术来提高在小子区域的参数估计精度。最终，能够检测空间不均匀参数变化的模拟数据进行了推理验证。