Fully Differentiable Lagrangian Convolutional Neural Network for Continuity-Consistent Physics-Informed Precipitation Nowcasting
<|reference_start|>Fully Differentiable Lagrangian Convolutional Neural Network for Continuity-Consistent Physics-Informed Precipitation Nowcasting: 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.<|reference_end|>
@article{pavlík2024fully,
    title={Fully Differentiable Lagrangian Convolutional Neural Network for
  Continuity-Consistent Physics-Informed Precipitation Nowcasting},
    author={Peter Pavl'ik, Martin V'yboh, Anna Bou Ezzeddine, Viera Rozinajov'a},
    journal={arXiv preprint arXiv:2402.10747},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.10747},
    primaryClass={cs.LG cs.AI cs.CV}
}