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X-WR-CALDESC:UMD - Earth System Science Interdisciplinary Center
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DTSTART;TZID=America/New_York:20221219T140000
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CREATED:20230905
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SUMMARY:Physics to Machine Learning and Machine Learning Back to Physics
DESCRIPTION:\nProf. Pierre Gentine\nColumbia University\nMonday December 19, 2022, 2 PM ET\nAbstract:\nOver the last couple of years, we have witnessed an explosion in the use of machine learning for Earth system science application ranging from Earth monitoring to modeling. Machine learning has shown tremendous success in emulating complex physics such as atmospheric convection or terrestrial carbon and water fluxes using satellite or high-fidelity simulations in a supervised framework. However, machine learning, especially deep learning, is opaque (the so-called black box issue) and thus a question remains: what (new) understanding have we really developed? I will here illustrate the value of machine learning for specific examples and some of the needed advances in machine learning to push climate science forward.\nBiosketch:\nPierre Gentine is the Maurice Ewing and J. Lamar Worzel professor of geophysics in the departments of Earth and Environmental Engineering and Earth and Environmental Sciences at Columbia University. He studies the terrestrial water and carbon cycles and their changes with climate change. Pierre Gentine is the recipient of the National Science Foundation (NSF), NASA and Department of energy (DOE) early career awards, as well as the American Geophysical Union Global Environmental Changes Early Career, Macelwane medal and American Meteorological Society Meisinger award. He is the director of the new NSF Science and Technology Center (STC) for Learning the Earth with Artificial intelligence and Physics (LEAP), the largest funding mechanism of the NSF.\n \nWebinar:\nEvent site: https://go.umd.edu/gentine\nZoom Webinar: https://go.umd.edu/gentinewebinar ( https://go.umd.edu/gentinewebinar )\nZoom Meeting ID: 913 6121 9329\nZoom password: essic\nUS Toll: +13017158592\nGlobal call-in numbers: https://umd.zoom.us/u/aMElEpvNu ( https://umd.zoom.us/u/aMElEpvNu )\nFor IT assistance:\nCazzy Medley: cazzy@umd.edu\nResources:\nSeminar schedule & archive: https://go.umd.edu/essicseminar ( https://go.umd.edu/essicseminar )\nSeminar Google calendar: https://go.umd.edu/essicseminarcalendar ( https://go.umd.edu/essicseminarcalendar )\nSeminar recordings on Youtube: https://www.youtube.com/user/ESSICUMD ( https://www.youtube.com/user/ESSICUMD )\n
URL:https://essic.umd.edu/events/physics-to-machine-learning-and-machine-learning-back-to-physics/
ORGANIZER;CN=John Xun Yang:MAILTO:jxyang@umd.edu
CATEGORIES:Fall 2022
ATTACH;FMTTYPE=image/png:https://essic.umd.edu/wp-content/uploads/2023/09/headshot_Pierre.Gentine.png
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