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<div id="divRplyFwdMsg" dir="ltr"><font face="Calibri, sans-serif" style="font-size:11pt" color="#000000"><b>From:</b> Math & ACMS Student Services Office <mathadv@uw.edu><br>
<b>Sent:</b> Tuesday, March 28, 2023 8:55 PM<br>
<b>To:</b> mathmajors@uw.edu <mathmajors@uw.edu><br>
<b>Subject:</b> ACMS seminar - March 30 1pm Steve Brunton</font>
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<span class="x_ContentPasted0" style="font-size:10pt; margin:0px; color:black; background-color:white">ACMS is hosting a talk that you might be interested in. Please join us!</span></div>
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<div style="font-size:14px; margin:0px"><span class="x_ContentPasted0" style="font-size:10pt; margin:0px; background-color:white">Thursday, March 30</span></div>
<div style="font-size:14px; margin:0px"><span class="x_ContentPasted0" style="font-size:10pt; margin:0px; background-color:white"><a href="https://www.me.washington.edu/facultyfinder/steve-brunton" data-auth="NotApplicable" data-loopstyle="link" title="https://www.me.washington.edu/facultyfinder/steve-brunton" data-safelink="true" data-linkindex="0" class="x_ContentPasted0" style="margin:0px">Professor
Steve<span> </span><span data-markjs="true" class="x_markwdywt04bl" data-ogac="" data-ogab="" data-ogsc="" data-ogsb="" style="margin:0px">Brunton</span></a><span class="x_ContentPasted0" style="margin:0px"> </span>from Mechanical Engineering to talk about
machine learning and dynamic systems</span></div>
<div style="font-size:14px; margin:0px"><span class="x_ContentPasted0" style="font-size:10pt; margin:0px; background-color:white">1-1:50pm in
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MGH 241</a></span></div>
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<div style="font-size:14px; margin:0px"><span class="x_ContentPasted0" style="font-size:10pt; margin:0px">All ACMS & MATH majors are encouraged to attend. <span class="x_ContentPasted2" style="display:inline!important; background-color:rgb(255,255,255)">There
will be snacks!</span></span></div>
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<span class="x_ContentPasted0" style="font-kerning:none; margin:0px">Title: Machine Learning for Scientific Discovery</span></p>
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<span class="x_ContentPasted0" style="font-kerning:none; margin:0px">Abstract: Accurate and efficient nonlinear dynamical systems models are essential understand, predict, estimate, and control complex natural and engineered systems. In this talk, I will explore
how machine learning may be used to develop these models purely from measurement data. We will emphasize techniques that yield models that are interpretable and generalizable, capturing the essential “physics” of the system. We also discuss the importance
of learning effective coordinate systems in which the dynamics may be learned. This machine learning modeling approach will be demonstrated on a range of challenging modeling problems, for example in fluid dynamics. <span class="x_ContentPasted0" style="margin:0px"> </span></span><span class="x_ContentPasted0" style="font-kerning:none; margin:0px">Because
fluid dynamics is central to transportation, health, and defense systems, we will emphasize the importance of machine learning solutions that are interpretable, explainable, generalizable, and that respect known physics.</span></p>
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<span style="font-kerning:none; margin:0px"><span class="x_ContentPasted0" style="margin:0px; background-color:white">We are making an effort to reduce the number of emails you receive from our office. We do receive a lot of announcements and have been posting
those here: </span><a href="https://uwmathandacms.wordpress.com/blog/" data-auth="NotApplicable" data-safelink="true" data-linkindex="2" class="x_ContentPasted0" style="margin:0px; background-color:white">https://uwmathandacms.wordpress.com/blog/</a></span></p>
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<span style="font-family:Arial,Helvetica,sans-serif; font-size:10pt; color:rgb(0,0,0); background-color:rgba(0,0,0,0)">Best regards,</span><br>
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