Fully Differentiable Lagrangian Convolutional Neural Network for
  Continuity-Consistent Physics-Informed Precipitation Nowcasting
Peter Pavlík Martin Výboh Anna Bou Ezzeddine Viera Rozinajová
This paper presents a convolutional neural network model for precipitation
nowcasting that combines data-driven learning with physics-informed domain
knowledge. We propose LUPIN, a Lagrangian Double U-Net for Physics-Informed
Nowcasting, that draws from existing extrapolation-based nowcasting methods and
implements the Lagrangian coordinate system transformation of the data in a
fully differentiable and GPU-accelerated manner to allow for real-time
end-to-end training and inference. Based on our evaluation, LUPIN matches and
exceeds the performance of the chosen benchmark, opening the door for other
Lagrangian machine learning models.