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DTSTART;TZID=America/Los_Angeles:20250408T100000
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DTSTAMP:20260405T081744
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LAST-MODIFIED:20250224T213115Z
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SUMMARY:FLUXNET-ECN-Webinar: Two for One: Partitioning CO2 Fluxes and Learning the GPP-SIF Relationship Using Machine Learning
DESCRIPTION:Title: Two for One: Partitioning CO2 Fluxes and Learning the GPP-SIF Relationship Using Machine Learning \nDate and Time: April 8 at 1 PM EST \nSpeaker: Weiwei Zhan\, Department of Earth and Environmental Engineering\, Columbia University
URL:https://ameriflux-new.hyperarts.com/event/fluxnet-ecn-webinar-two-for-one-partitioning-co2-fluxes-and-learning-the-gpp-sif-relationship-using-machine-learning/
LOCATION:virtual
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20241120T090000
DTEND;TZID=America/Los_Angeles:20241120T103000
DTSTAMP:20260405T081744
CREATED:20241023T162522Z
LAST-MODIFIED:20241023T162522Z
UID:10000267-1732093200-1732098600@ameriflux-new.hyperarts.com
SUMMARY:FLUXNET-ECN Workshop: Knowledge-Guided Machine Learning for Estimating Carbon Fluxes using Eddy Covariance Data: Seminar and Tutorial
DESCRIPTION:We are excited to announce an upcoming FLUXNET-ECN Seminar/Workshop\, a community event co-sponsored by the FLUXNET Early Career Network\, AmeriFlux Management Project (AMP)\, and members of the FLUXNET communities. The workshop focuses on Knowledge-Guided Machine Learning (KGML) for Estimating Carbon Fluxes using Eddy Covariance Data: Seminar and Tutorial. Our invited speaker is Dr. Licheng Liu at the University of Minnesota. \nThis seminar will explore methods to open the ‘black box’ of machine learning by integrating scientific knowledge from mechanism-based models into advanced machine learning frameworks\, known as Knowledge-Guided Machine Learning (KGML). The focus will be on utilizing eddy covariance data for accurate\, efficient\, and interpretable simulations of carbon cycles.  \nTutorials will be provided based on the Nature Communications paper led by Dr. Liu (https://www.nature.com/articles/s41467-023-43860-5)\, along with its associated code. Participants are encouraged to review the paper and try the ‘five_steps_training’ code before the seminar. \nShort Bio:\nDr. Licheng Liu is a senior research scientist leading the KGML division in the AI-CLIMATE Institute at the University of Minnesota. Dr. Liu’s research centers on enhancing our understanding of greenhouse gas (GHG) sources and sinks in agricultural and natural ecosystems\, to explain their roles in climate change and provide actionable insights for mitigation strategies\, by integrating advanced analytical tools such as process-based models and KGML\, multi-source data from modern sensing techniques\, and AI-accelerated optimization algorithms in decision making (https://bbe.umn.edu/people/licheng-liu).  \nRegister for this webinar: https://lbnl.zoom.us/meeting/register/tJAldOugqj4uHNTMDM0L0Hll4E0fRVaEZrJQ
URL:https://ameriflux-new.hyperarts.com/event/fluxnet-ecn-workshop-knowledge-guided-machine-learning-for-estimating-carbon-fluxes-using-eddy-covariance-data-seminar-and-tutorial/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20241101T090000
DTEND;TZID=America/Los_Angeles:20241101T100000
DTSTAMP:20260405T081744
CREATED:20241023T162515Z
LAST-MODIFIED:20241023T162515Z
UID:10000266-1730451600-1730455200@ameriflux-new.hyperarts.com
SUMMARY:[FLUXNET-ECN Workshop] AI/Machine Learning for Eddy Covariance Data
DESCRIPTION:We are excited to announce an upcoming FLUXNET-ECN Seminar/Workshop\, a community event co-sponsored by the FLUXNET Early Career Network\, AmeriFlux Management Project (AMP)\, and members of the FLUXNET communities. The workshop focuses on AI and Machine Learning Applications for Eddy Covariance Data. Two featured speakers are Dr. Pamela Weisenhorn and Dr. Benjamin Stocker. \nThis seminar will explore the integration of advanced artificial intelligence (AI) and machine learning (ML) techniques into the analysis of eddy covariance (EC) data. It will also include tutorials based on https://stineb.github.io/ml4ec_workshop/\, which Dr. Stocker developed. A friendly reminder is to walk through the tutorial before the seminar for better learning experiences.  \nRegister for this webinar: https://lbnl.zoom.us/meeting/register/tJckf–uqD8sGtdDYcOVNMqbAkoY9znu4bV_\nDate: Nov.1\, 2024\nTime: 9 AM Pacific Time\, 11 AM US Central Time\, 5 PM Central European Time   \nContact us at:  fluxnet-ecn-owner@fluxdata.org \nHope to see you there! \nBest\,\nXiangmin (Sam) Sun and Bailey Murphy\,\nOn behalf of the FLUXNET-ECN organizing committee
URL:https://ameriflux-new.hyperarts.com/event/fluxnet-ecn-workshop-ai-machine-learning-for-eddy-covariance-data/
LOCATION:virtual
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210415T080000
DTEND;TZID=America/Los_Angeles:20210416T120000
DTSTAMP:20260405T081744
CREATED:20210323T235509Z
LAST-MODIFIED:20210324T215854Z
UID:10000215-1618473600-1618574400@ameriflux-new.hyperarts.com
SUMMARY:FLUXNET Spring Workshop
DESCRIPTION:We are honored to invite you to participate in the FLUXNET Spring Workshop\, which will occur on the days 15 and 16 of April 2021. The workshop promotes the Year of Water Flux with a webinar on the first day (9 am to 12 pm PT) and a Python Course (8 am to 12 pm PT) on the second day. The webinar session will host a 4-research panel to discuss the intersections and opportunities of flux measurements\, modeling\, and remote sensing. More information will be available later this week on the FLUXNET website Blog.
URL:https://ameriflux-new.hyperarts.com/event/fluxnet-spring-workshop/
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