  Deep neural networks (DNNs) are increasingly used to solve partial
differential equations (PDEs) that naturally arise while modeling a wide range
of systems and physical phenomena. However, the accuracy of such DNNs decreases
as the PDE complexity increases and they also suffer from spectral bias as they
tend to learn the low-frequency solution characteristics. To address these
issues, we introduce Parametric Grid Convolutional Attention Networks (PGCANs)
that can solve PDE systems without leveraging any labeled data in the domain.
The main idea of PGCAN is to parameterize the input space with a grid-based
encoder whose parameters are connected to the output via a DNN decoder that
leverages attention to prioritize feature training. Our encoder provides a
localized learning ability and uses convolution layers to avoid overfitting and
improve information propagation rate from the boundaries to the interior of the
domain. We test the performance of PGCAN on a wide range of PDE systems and
show that it effectively addresses spectral bias and provides more accurate
solutions compared to competing methods.
