[Acmsmajors] Winter ACMS Seminar - Jan. 12 talk/social

[Math & ACMS Student Services Office]

Dear all,

Unfortunately, our speaker for tomorrow needed to cancel.  We will reschedule and send out a new announcement once a date is confirmed.

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: Monday, January 9, 2023 8:38 AM
To: acmsmajors at uw.edu <acmsmajors at uw.edu>
Subject: Winter ACMS Seminar - Jan. 12 talk/social

Dear ACMS Majors,

Welcome to winter quarter!  We hope you had a great winter 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 17431) 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.

Our first guest speaker is on
Thursday, January 12
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 CSE2<https://www.washington.edu/students/maps/map.cgi?CSE2> G20
All ACMS majors are encouraged to attend, even if not enrolled in the seminar.


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.

Steve Brunton | Mechanical Engineering<https://www.me.washington.edu/facultyfinder/steve-brunton>
Education. Ph.D. in Mechanical and Aerospace Engineering, Princeton University, 2012; B.S. in Mathematics, Minor in Control and Dynamical Systems, California ...
www.me.washington.edu
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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