Model-driven Machine Learning for Climate and Earth Science
Understanding and predicting the complex dynamics of the Earth’s atmosphere, ocean, land and ice.
This Helmholtz AI young investigator group, led by David Greenberg, is located within the Institute of Coastal Systems - Analysis and Modeling, at the Helmholtz Zentrum Hereon (Hereon). Their research focuses on the Earth system at the boundary between physics-based and machine learning approaches, to gain insights unavailable to either approach alone.
The research group also organizes a weekly seminar on machine learning at Hereon, aimed at the scientific communities of the Helmholtz Institutes and the Hamburg area.
Physical models. Numerical simulations based on known physics simulators handle complex systems well, but struggle with data assimilation, parameter tuning and uncertainty quantification.
Machine Learning. Conversely, machine learning techniques can absorb and process large datasets, but typically ignore physics and generalize poorly to new scenarios.
Model-driven Machine Learning. We develop hybrid methods that combine the advantages of deep learning and physical modeling in a Bayesian framework.