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<b>FYI</b></div>
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<b>From:</b><span style="background-color: rgb(255, 255, 255);"> Sanjana Achan <sachan@gmu.edu></span></div>
<div class="elementToProof" style="color: rgb(0, 0, 0);"><span style="font-family: Calibri, sans-serif; font-size: 14.666667px;"><b>Sent:</b></span><span style="font-family: Calibri, sans-serif; font-size: 14.666667px; background-color: rgb(255, 255, 255);"> Thursday,
April 18, 2024 12:51</span><span style="font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-size: 12pt;"><br>
</span><span style="font-family: Calibri, sans-serif; font-size: 14.666667px;"><b>To:</b></span><span style="font-family: Calibri, sans-serif; font-size: 14.666667px; background-color: rgb(255, 255, 255);"> esip-machinelearning@lists.esipfed.org <esip-machinelearning@lists.esipfed.org></span><span style="font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-size: 12pt;"><br>
</span><span style="font-family: Calibri, sans-serif; font-size: 14.666667px;"><b>Cc:</b></span><span style="font-family: Calibri, sans-serif; font-size: 14.666667px; background-color: rgb(255, 255, 255);"> Ziheng Sun <zsun@gmu.edu>; Chaopeng Shen <shen.chaopeng@gmail.com></span></div>
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<b>Subject:</b><span style="background-color: rgb(255, 255, 255);"> Reminder- Upcoming ESIP Machine Learning Cluster Meeting</span></div>
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Dear Team,</div>
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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.</div>
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Please find the meeting link in the ESIP calendar under the name "Machine Learning."</div>
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<a href="https://www.esipfed.org/community-calendar/" id="OWA3d9eed0f-5b87-2eb0-bec7-aa4043085f8f" class="x_OWAAutoLink" shash="NoAwumnie2j3voSQImlNfKNcMjxwtE3LFnVRzgST9RED27kMdM04SfQ7JJN94vessWd8Rif52Ixgi9o/7puXweqPNoFRB0SjT2Qgj4MMPuORbTVCJHFOd2oBB/hOH5A7RvKk3TefuTLKRngkhwyaS/zuV5DYyjH84oLXxiqo27Q=" originalsrc="https://www.esipfed.org/community-calendar/" data-auth="Verified" data-loopstyle="linkonly">https://www.esipfed.org/community-calendar/</a></div>
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<span style="font-weight:300"><a href="https://www.esipfed.org/community-calendar/" class="x_OWAAutoLink" data-auth="Verified" data-loopstyle="linkonly" style="text-decoration:none" id="OWAa7b71fab-b73b-3daa-d049-bb868c984ee9">Events - ESIP</a></span></div>
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Events Archive - ESIP</div>
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www.esipfed.org</div>
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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.</div>
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To give you a sneak peek of what to expect, Shen will be sharing her expertise on the topic,
<b>"Machine learning and physics-informed ML for global water sustainability and water quality."</b> 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:</div>
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<a href="https://www.nature.com/articles/s43017-023-00450-9" id="OWA5dff2aa2-1396-7dcf-2dc0-5e506d8f7a51" class="x_OWAAutoLink" shash="cu/AHQFgqFCFYqRDm6KfEOt4dLvVKqzqcYi7ggz82R6k2xndCX0CoB3Jg4NdCQl16vnvygOua4662Z6LucJX3a/dspb9KqPx2D1zw+b5hzLmAdFRAH8fzlCr6ylL4FUNlkQQahLzZg8mK3Hb9US4cJnWfo7F2vlNU6k4hhitY58=" originalsrc="https://www.nature.com/articles/s43017-023-00450-9" data-auth="Verified" data-loopstyle="linkonly">https://www.nature.com/articles/s43017-023-00450-9</a></div>
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<div style="direction:ltr;margin-right:12px"><a href="https://www.nature.com/articles/s43017-023-00450-9" class="x_OWAAutoLink" data-auth="Verified" data-loopstyle="linkonly" id="OWA0d7a9cc7-9e92-dff4-1281-bcc584a3538a"><img width="240" height="93" style="width:240px;height:93px;display:block" src="https://media.springernature.com/m685/springer-static/image/art%3A10.1038%2Fs43017-023-00450-9/MediaObjects/43017_2023_450_Fig1_HTML.png"></a></div>
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<span style="font-weight:300"><a href="https://www.nature.com/articles/s43017-023-00450-9" class="x_OWAAutoLink" data-auth="Verified" data-loopstyle="linkonly" style="text-decoration:none" id="OWAbdd068bb-58bd-f194-2f17-e856069904de">Differentiable modelling
to unify machine learning and physical models for geosciences - Nature Reviews Earth & Environment</a></span></div>
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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.</div>
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www.nature.com</div>
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Our agenda for the ESIP Machine Learning Cluster Meeting is:</div>
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Guest speaker</div>
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Q&A</div>
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Anything else?</div>
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If you have more things to discuss, please feel free to raise them.</div>
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Sincerely,</div>
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Sanjana Achan,</div>
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Research Assistant.</div>
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