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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

The Utility of Physics-Informed Machine Learning in Solar Power Production Forecasting

Peter Pavlík, Franz M. Rohrhofer, Bernhard C. Geiger

Physics-informed machine learning (PIML) and hybrid modeling have attracted significant attention as approaches for combining physical knowledge with the flexibility of deep learni…

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…