Fully Differentiable Lagrangian Convolutional Neural Network for Continuity-Consistent Physics-Informed Precipitation Nowcasting

arXiv:2402.10747v1 Announce Type: new
Abstract: 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, …

abstract arxiv consistent continuity convolutional neural network cs.ai cs.cv cs.lg data data-driven differentiable domain domain knowledge knowledge network neural network nowcasting paper physics physics-informed precipitation transformation type