  We 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 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.
