BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Markus Reichstein (MPI for Biogeochemistry) DTSTART:20221124T170000Z DTEND:20221124T180000Z DTSTAMP:20260423T003246Z UID:MPML/87 DESCRIPTION:Title: In tegrating Machine Learning with System Modelling and Observations for a be tter understanding of the Earth System\nby Markus Reichstein (MPI for Biogeochemistry) as part of Mathematics\, Physics and Machine Learning (IS T\, Lisbon)\n\n\nAbstract\nThe Earth is a complex dynamic networked system . Machine learning\, i.e. derivation of computational models from data\, h as already made important contributions to predict and understand componen ts of the Earth system\, specifically in climate\, remote sensing and envi ronmental sciences. For instance\, classifications of land cover types\, p rediction of land-atmosphere and ocean-atmosphere exchange\, or detection of extreme events have greatly benefited from these approaches. Such data- driven information has already changed how Earth system models are evaluat ed and further developed. However\, many studies have not yet sufficiently addressed and exploited dynamic aspects of systems\, such as memory effec ts for prediction and effects of spatial context\, e.g. for classification and change detection. In particular new developments in deep learning off er great potential to overcome these limitations. Yet\, a key challenge an d opportunity is to integrate (physical-biological) system modeling approa ches with machine learning into hybrid modeling approaches\, which combine s physical consistency and machine learning versatility. A couple of examp les are given with focus on the terrestrial biosphere\, where the combinat ion of system-based and machine-learning-based modelling helps our underst anding of aspects of the Earth system.\n LOCATION:https://researchseminars.org/talk/MPML/87/ END:VEVENT END:VCALENDAR