[Acmsmajors] ACMS seminar - March 30 1pm Steve Brunton

[Math & ACMS Student Services Office]

See you all at 1pm today!

Best regards,

Math & ACMS Advising
Department of Mathematics
Padelford Hall C-36

Drop-in Advising Hours<https://math.washington.edu/advising-academic-support#ZoomDrop-in>
Math and ACMS Blog<https://uwmathandacms.wordpress.com/blog/>
math.washington.edu<https://math.washington.edu/>      acms.washington.edu<http://acms.washington.edu/>

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________________________________
From: Math & ACMS Student Services Office <mathadv at uw.edu>
Sent: Tuesday, March 28, 2023 8:55 PM
To: acmsmajors at uw.edu <acmsmajors at uw.edu>
Subject: ACMS seminar - March 30 1pm Steve Brunton

Hello ACMS Majors!

Welcome to spring quarter!  We hope you had a great spring break.  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: https://uwmathandacms.wordpress.com/blog/

This quarter we have our ACMS Seminar course running, MATH 282 (sln 16932) for 1 credit cr/nc grading.  The seminar will meet just a few times in the quarter on Thursdays from 1-1:50pm when we have a guest speaker.


Thursday, March 30
Professor Steve Brunton<https://www.me.washington.edu/facultyfinder/steve-brunton> from Mechanical Engineering to talk about machine learning and dynamic systems
1-1:50pm in MGH 241<http://uw.edu/maps/?mgh>
[http://s3-us-west-2.amazonaws.com/uw-s3-cdn/wp-content/uploads/sites/10/2019/06/21094817/Univ-of-Washington_Memorial-Way.jpg]<http://uw.edu/maps/?mgh>
UW Campus Map<http://uw.edu/maps/?mgh>
University of Washington
uw.edu


All ACMS & MATH majors are encouraged to attend.  There will be snacks!


Title: Machine Learning for Scientific Discovery


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.  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.


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: https://uwmathandacms.wordpress.com/blog/


Best regards,

Math & ACMS Advising
Department of Mathematics
Padelford Hall C-36

Drop-in Advising Hours<https://math.washington.edu/advising-academic-support#ZoomDrop-in>
Math and ACMS Blog<https://uwmathandacms.wordpress.com/blog/>
math.washington.edu<https://math.washington.edu/>      acms.washington.edu<http://acms.washington.edu/>

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