  Artificial intelligence techniques are considered an effective means to
accelerate flow field simulations. However, current deep learning methods
struggle to achieve generalization to flow field resolutions while ensuring
computational efficiency. This paper presents a deep learning approach for
rapid prediction of two types of subsonic flow fields with different
resolutions. Unlike convolutional neural networks, the constructed feature
extractor integrates features of different spatial scales along the channel
dimension, reducing the sensitivity of the deep learning model to resolution
while improving computational efficiency. Additionally, to ensure consistency
between the input and output resolutions of the deep learning model, a memory
pooling strategy is proposed, which ensures accurate reconstruction of flow
fields at any resolution. By conducting extensive qualitative and quantitative
analyses on a given test dataset, it is demonstrated that the proposed deep
learning model can achieve a three-order-of-magnitude speedup compared to
CPU-based solvers while adapting to flow fields of arbitrary resolutions.
Moreover, the prediction accuracy for pressure exceeds 99\%, laying the
foundation for the development of large-scale models in the field of
aerodynamics.
