title: AA-PINN: ATTENTION AUGMENTED PHYSICS INFORMED NEURAL NETWORKS
abstract: Physics Informed Neural Networks has been quite successful in modelling the complex nature of fluid flow. Computational Fluid Dynamics using parallel processing
  algorithms on GPUs have considerably reduced the time to solve the Navier Stokes
  Equations. CFD based approaches uses approximates to make the modelling easy
  but it comes at the cost of decrease in accuracy. In this paper, we propose an
  attention based network architecture named AA-PINN to model PDEs behind fluid
  flow. We use a combination of channel and spatial attention module. We propose a
  novel loss function which is more robust in handling the initial as well as boundary
  conditions imposed. Using evaluation metrics like RMSE, divergence and thermal
  kinetic energy, our network outperforms previous PINNs for modelling Navier
  Stokes and Burgers Equation.
authors: Abhinav Sagar
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