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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 […]
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, […]
Join the AI + Environment Summit: A Day to Innovate, Inspire, and Impact! After the incredible success of last year's edition, the AI + Environment Summit is back! Mark your calendars for October 2nd and join us at the Innovation Park in Dubendorf, where we’ll come together to explore the intersection of artificial intelligence and […]
