Physics-informed machine learning for nowcasting extreme rainfall

The thesis explores an innovative technique for enhancing the precision of short-term weather forecasts, particularly in predicting extreme weather phenomena, which present a notable challenge for existing models such as PySTEPS due to their volatile behavior. Leveraging precipitation and meteorological data sourced from the Royal Netherlands...

Physical pooling functions in graph neural networks for molecular property prediction

Graph neural networks (GNNs) are emerging in chemical engineering for the end-to-end learning of physicochemical properties based on molecular graphs. A key element of GNNs is the pooling function which combines atom feature vectors into molecular fingerprints. Most previous works use a standard pooling function to predict a variety of...