[Esip-machinelearning] Fw: Reminder- Upcoming ESIP Machine Learning Cluster Meeting

[Ziheng Sun]

FYI

From: Sanjana Achan <sachan at gmu.edu>
Sent: Thursday, April 18, 2024 12:51
To: esip-machinelearning at lists.esipfed.org <esip-machinelearning at lists.esipfed.org>
Cc: Ziheng Sun <zsun at gmu.edu>; Chaopeng Shen <shen.chaopeng at gmail.com>
Subject: Reminder- Upcoming ESIP Machine Learning Cluster Meeting

Dear Team,

This is a reminder that our ESIP Machine Learning Cluster Monthly Meeting will be on Friday, 4/19/2024, from 12:00 PM to 1:00 PM EST.

Please find the meeting link in the ESIP calendar under the name "Machine Learning."
https://www.esipfed.org/community-calendar/
[https://www.esipfed.org/wp-content/uploads/2023/12/ESIP-featured-image.png]<https://www.esipfed.org/community-calendar/>
Events - ESIP<https://www.esipfed.org/community-calendar/>
Events Archive - ESIP
www.esipfed.org
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The highlight of this meeting is the presence of an esteemed guest speaker, Chaopeng Shen. Shen is an associate professor in the Department of Civil and Environmental Engineering at Pennsylvania State University. He has dedicated his career to the field of hydrology, with a passion for unraveling the intricate interactions between water systems and other vital subsystems like ecosystems, energy cycles, and the solid earth.  His contribution also includes the creation of the Process-based Adaptive Watershed Simulator (PAWS), an open-source tool for large-scale hydrological simulations. His aim to provide actionable insights for sustainable water management globally is reflected in his numerous publications.

To give you a sneak peek of what to expect, Shen will be sharing her expertise on the topic, "Machine learning and physics-informed ML for global water sustainability and water quality." To prepare for the meeting, we encourage you to take a look at one of Shen's influential papers. You can find it right here:
https://www.nature.com/articles/s43017-023-00450-9
[https://media.springernature.com/m685/springer-static/image/art%3A10.1038%2Fs43017-023-00450-9/MediaObjects/43017_2023_450_Fig1_HTML.png]<https://www.nature.com/articles/s43017-023-00450-9>
Differentiable modelling to unify machine learning and physical models for geosciences - Nature Reviews Earth & Environment<https://www.nature.com/articles/s43017-023-00450-9>
Differentiable modelling is an approach that flexibly integrates the learning capability of machine learning with the interpretability of process-based models. This Perspective highlights the potential of differentiable modelling to improve the representation of processes, parameter estimation, and predictive accuracy in the geosciences.
www.nature.com

Our agenda for the ESIP Machine Learning Cluster Meeting is:
Guest speaker
Q&A
Anything else?
If you have more things to discuss, please feel free to raise them.

Sincerely,
Sanjana Achan,
Research Assistant.
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