1 citations · 1 across the 3 of their papers we have counts for
4 papers
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…
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…
Do Echo Top Heights Improve Deep Learning Nowcasts?
Peter Pavlík, Marc Schleiss, Anna Bou Ezzeddine +1
Precipitation nowcasting -- the short-term prediction of rainfall using recent radar observations -- is critical for weather-sensitive sectors such as transportation, agriculture,…
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…