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Advection Augmented Convolutional Neural Networks
Paper Details
Published: 2024/06/27
DOI: 10.48550/arXiv.2406.19253
ARXIV ID: 2406.19253v1
Many problems in physical sciences are characterized by the prediction of space-time sequences. Modern techniques for the solution of these problems typically combine Convolution Neural Networks (CNN) architecture with a time prediction mechanism. However, oftentimes, such approaches underperform in the long-range propagation of information and lack explainability. In this work, the authors introduce a physically inspired architecture for the solution of such problems. Namely, they propose to augment CNNs with advection by designing a novel semi-Lagrangian push operator and show that the proposed operator allows for the non-local transformation of information compared with standard convolutional kernels.
