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<h1 id="scientific-machine-learning-mini-course">Scientific Machine Learning Webinar Series</h1>

<h2 id="website-change" style="color:#ff0000"> The SciML webinar has moved to the University of Michigan's MICDE. Find the website, calendar,
Zoom link and talks at <a href="https://micdedev.wpengine.com/news-events/sciml-webinar-series/">
https://micdedev.wpengine.com/news-events/sciml-webinar-series/ </a>. If you were already on our mailing list, you
do not have to sign up again.
</h2>
<br>
<p>This webinar series and panel events are organized by <a href="https://www.andrew.cmu.edu/user/mphuthi/">Keith Phuthi</a>, Varun Shankar and <a href="https://www.andrew.cmu.edu/user/venkatv/">Venkat Viswanathan</a> with the goal of cross-pollinating ideas between the various emerging methods at the intersection of physics and machine learning.</p>
<p>Webinar Format: Presenters can use the opportunity to showcase a paper or two with an explicit focus on the methodology and approach. Duration: 40 minutes of methodology + 20 minutes of implementation (code) walk-through + 20 minutes of questions. Invited session chairs will guide the discussion along with offering their perspective on the field. The Q&amp;A session is typically very interactive with a small group of enthusiastic audience.</p>
<p>Seminar Time: Thursdays 11 am to 12:30 pm Eastern Time </p>

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<h2 id="How To Join">How To Join</h2>
<!-- <h2 id="How To Join">How To Join</h2>
<p><a href="https://calendar.google.com/calendar/u/0/r?cid=c_ij54tumnc69etaoimap83sflsc@group.calendar.google.com">Add Webinar Calendar</a><br />
<a href="https://cmu.zoom.us/j/99244798052?pwd=dTlCYkpHK3kzdStEd3FuWWU5amJ4dz09">Zoom Link</a><br />
Webinar ID: 992 4479 8052<br />
Passcode: 919401</p>
Passcode: 919401</p> -->

<h4 id="How To Join">Suggest speakers: </h4>
<p><a href="https://docs.google.com/forms/d/e/1FAIpQLSdv6yyHQ2XSfOOqNlG0QZRlDA11BItCz1wBTZmr16an9qx2LQ/viewform?usp=sf_link"> Form </a></p>

<br/>
<h2 id="tentative-speakers-and-topics">Upcoming/Ongoing Events</h2>
<h2 id="tentative-speakers-and-topics"> Past Webinars </h2>
<h4 id="ML-embed">Spring 2023:</h4>

<ul>
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</li>
</ul>


<h2 id="past-speakers-and-topics">Past Webinars</h2>

<h4 id="ML-embed">Fall 2022:</h4>
<ul>
<li>September 22: <a href="https://www.linkedin.com/in/ilyes-batatia-725ab5197/"> Batatia Ilyes (ENS Paris Saclay)</a>
Expand Down Expand Up @@ -541,12 +545,12 @@ <h2 id="Resources">Resources</h2>
<h2 id="conception">Conception</h2>
<p>A panel discussion on the topic <a href="https://pubs.acs.org/doi/full/10.1021/acsenergylett.8b02278">Machine Learning Based Approaches to Accelerate Energy Materials Discovery and Optimization</a> crystallized important considerations while applying machine learning methods to limited-data engineering applications. Often it's useful to synergistally stack the ML models to the extent possible with the known physics of the problem for effective learning even in low-data regimes.</p>
<h2 id="questions">Questions?</h2>
<p>Email the organizers at mphuthi[at]andrew.cmu.edu, varunshankar[at]cmu.edu and venkvis[at]cmu.edu</p>
<p>Email the organizers at mkphuthi[at]cmu.edu and venkvis[at]cmu.edu</p>

<h2 id="Video Recordings">Video Recordings</h2>
A Google Drive link to the Video recordings are available on request on the slack or by emailing the organizers.

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Expand All @@ -567,20 +571,21 @@ <h2>Subscribe to the SciML Newsletter</h2>
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