5 papers
AIFS-TC: A simple correction competitive with the operational frontier for tropical cyclone intensity forecasting
Anna Allen, Wessel P. Bruinsma, Michael Maier-Gerber +3
AI weather models are in the process of revolutionising weather forecasting. While these models have been shown to achieve superior performance to physics-based NWP in forecasting…
Accurate and scalable exchange-correlation with deep learning
Giulia Luise, Chin-Wei Huang, Thijs Vogels +25
Density Functional Theory (DFT) underpins much of modern computational chemistry and materials science. Yet, the reliability of DFT-derived predictions of experimentally measurable…
Noise-Aware Differentially Private Regression via Meta-Learning
Ossi Räisä, Stratis Markou, Matthew Ashman +4
Many high-stakes applications require machine learning models that protect user privacy and provide well-calibrated, accurate predictions. While Differential Privacy (DP) is the go…
A Foundation Model for the Earth System
Cristian Bodnar, Wessel P. Bruinsma, Ana Lucic +15
Reliable forecasts of the Earth system are crucial for human progress and safety from natural disasters. Artificial intelligence offers substantial potential to improve prediction…
Approximately Equivariant Neural Processes
Matthew Ashman, Cristiana Diaconu, Adrian Weller +2
Equivariant deep learning architectures exploit symmetries in learning problems to improve the sample efficiency of neural-network-based models and their ability to generalise. How…