The COP29 Declaration on Green Digital Action highlights the critical role of digital technologies in combating climate change. The workshop will focus on various applications of neural networks within environmental science and sustainable development, addressing key issues such as climate prediction and the critical need for explainability in such scenarios. Climatologists will share insights on how neural networks are reshaping research methodologies in their field. Industry practitioners will highlight the application of foundation models in Earth system simulations, with a particular focus on weather-related processes. Distinguished scholars will contribute valuable perspectives on climate predictability and the challenge of developing transparent and reliable AI systems. It will provide a rare opportunity for interdisciplinary dialogue, connecting deep learning techniques with the most pressing environmental challenges.
Climate predicatbility
Interpretable Machine Learning
Climate Carbon Modelling
Physics Informed Machine Learning
Neural Operators
Neural ODE
Weather Forecasting
ML for Causal Inference in Climate Systems
Uncertainty Quantification in Climate Predictions
Transfer Learning for Climate Solutions
Time-series Analysis for Energy Systems
Interpretable Models in Climate Policy
Organizers
Maria Sofia Bucarelli
Sapienza, University of Rome
Francesco Caso
Sapienza, University of Rome
Andrea Drago
Sapienza, University of Rome
Christian Giannetti
Sapienza, University of Rome