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20242026
most citedFully Differentiable Lagrangian Convolutional Neural Network for Physics-Informed Precipitation Nowcasting

1 citations · 1 across the 3 of their papers we have counts for

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cs.LG2026

Assessing the Utility of Volumetric Motion Fields for Radar-based Precipitation Nowcasting with Physics-informed Deep Learning

Peter Pavlík, Anna Bou Ezzeddine, Viera Rozinajová

Estimating motion from spatiotemporal geoscientific data is a fundamental component of many environmental modeling and forecasting tasks. In this work, we propose a physics-informe…

cs.LG2024★ 1 cited

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

Peter Pavlík, Martin Výboh, Anna Bou Ezzeddine +1

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…