[CMU AI Seminar] Sep 21 at 12pm (Zoom) -- Stephan Hoyer (Google) -- Accelerating computational fluid dynamics with deep learning -- AI Seminar sponsored by Morgan Stanley

[Shaojie Bai]

Dear all,

Welcome to the CMU AI Seminar for the Fall 2021 semester! For the new
semester, we are excited to announce that *Morgan Stanley* has become the
new sponsor of the seminar series, which will start on September 21.

We look forward to seeing you *next Tuesday (9/21)* from *1**2:00-1:00 PM
(U.S. Eastern time)* for the next talk of our *CMU AI seminar*, sponsored
by Morgan Stanley <https://www.morganstanley.com/about-us/technology/>.

To learn more about the seminar series or see the future schedule, please
visit the seminar website <http://www.cs.cmu.edu/~aiseminar/>.

On 9/21, *Stephan Hoyer* (Google) will be giving a talk on "*Accelerating
Computational Fluid Dynamics with Deep Learning*" and share some of
the exciting progress in deep learning for scientific computing.

*Title*: Accelerating Computational Fluid Dynamics with Deep Learning

*Talk Abstract*: How can machine learning help large-scale scientific
simulation? Accurate simulation of fluids is important for problems like
engineering design and climate modeling, but is very computationally
demanding. In this talk, I'll give an overview of a line of research at
Google, where we've been using end-to-end deep learning to improve
approximations inside traditional numerical solvers. For 2D turbulent
flows, our models are up to two orders of magnitude faster than traditional
solvers with the same accuracy on the same hardware, and can still
generalize to very different types of flows from those on which they were
trained.

*Speaker Bio*:  Stephan Hoyer is a staff engineer at Google Research. He
works on deep learning for science, with a focus on physical simulations
and applications in climate/weather modeling. His research centers on the
hypothesis that automatic differentiation software, hardware accelerators
and deep learning are poised to transform traditional scientific computing,
by vastly accelerating and improving existing numerical models. He also
frequently contributes to open source tools for scientific computing in
Python, including JAX and NumPy. Before Google, he was a data scientist at
The Climate Corporation, and received his Ph.D in physics from UC Berkeley.

*Zoom Link*:
https://cmu.zoom.us/j/99723728452?pwd=YnBOa0ZDSXRxRmdSYTJiNGNVVFJ4UT09

Thanks,
Shaojie Bai (MLD)
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