AI Excellence Lecture on Hybrid & Causal Machine learning in the Earth sciences

ONLINE

Abstract: Most problems in Earth sciences aim to do inferences about the system, where accurate predictions are just a tiny part of the whole problem. Inferences mean understanding variables relations, deriving models that are physically plausible, that are simple parsimonious, and mathematically tractable. While machine learning models excel as approximators, they often disregard fundamental physics […]

AI Excellence Lecture on Deep learning and Process Understanding for Data-Driven Earth System Science

ONLINE

Abstract: For a better understanding of the Earth system we need a stronger integration of observations and (mechanistic) models. Classical model-data integration approaches start with a model structure and try to estimate states or parameters via data assimilation and inverse modelling, respectively. Sometimes, several model structures are employed and evaluated, e.g. in Bayesian model averaging, […]