Machine learning for earth and climate sciences

This program aims to model and understand the Earth system by combining machine learning with physical and process-based knowledge. Its research pillars include spatio-temporal modeling of climate dynamics, extreme event detection and attribution, hybrid physics-informed learning, generative Earth system modeling, data–model fusion, and interpretable climate forecasting.