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

Autor:	Pavlík, Peter, Výboh, Martin, Ezzeddine, Anna Bou, Rozinajová, Viera
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Computer Science - Computer Vision and Pattern Recognition I.2.1 J.2
Druh dokumentu:	Working Paper
Popis:	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. Comment: Submitted to ICML 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2402.10747 Zobrazit plný text záznamu View this record from Arxiv