[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]

Hi all,

Just a reminder that the CMU AI Seminar <http://www.cs.cmu.edu/~aiseminar/> is
tomorrow *12pm-1pm*:
https://cmu.zoom.us/j/99723728452?pwd=YnBOa0ZDSXRxRmdSYTJiNGNVVFJ4UT09.

*Stephan Hoyer (Google)* will be talking about some latest progress in
computational fluid dynamics, PDEs and deep learning! At the beginning of
the seminar tomorrow, the sponsor of our AI seminar (Morgan Stanley) will
also briefly introduce their AI team to kick off the fall 2021 series.

Thanks,
Shaojie

On Tue, Sep 14, 2021 at 1:30 PM Shaojie Bai <shaojieb at cs.cmu.edu> wrote:

> 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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