[Sds-seminars] [Sds-announce] Wednesday at 4: Tailin Wu on "Learning structured representations for accelerating scientific discovery and simulation"

[Dan Spielman]

TAILIN WU, Stanford University
Learning structured representations for accelerating scientific discovery
and simulation
Wednesday, February 22, 20234:00PM to 5:00PM
Mason Lab 211 see map
<http://maps.google.com/?q=9+Hillhouse+Ave%2C+New+Haven%2C+CT%2C+06511%2C+us>

9 Hillhouse Ave
New Haven, CT 06511
Website <https://tailin.org/>
Information and Abstract:

Across most disciplines of science, e.g., physics, chemistry, biomedicine,
materials, mechanical engineering, and energy, a most critical challenge is
that their simulations and discoveries are typically slow due to the
large-scale, complex and multi-scale nature of the system. In this talk, I
will introduce my research that tackles this challenge by developing
machine learning models with structured and efficient representations for
accelerating scientific discovery and simulation. To accelerate scientific
discovery, I developed neuro-symbolic methods which can distill the data
into human-interpretable symbolic knowledge (governing equations and
relational structures) and generalize to more complex data in inference. To
accelerate large-scale scientific simulations, I developed structured
representations to accelerate critical scientific simulations for fluid
dynamics, plasma science, and generic partial differential equations
(PDEs). For example, I developed a hybrid particle-fluid representation for
simulating a large-scale laser-plasma interaction in a national lab
facility that has important applications in physics, materials, and
biomedical science. Our model is able to simulate millions of particles per
time step, orders of magnitude faster than the classical solver, and
significantly reduce long-term prediction error compared to strong deep
learning baselines.

*BIO: *Tailin Wu is a postdoctoral scholar in the Computer Science
Department at Stanford University, working with Prof. Jure Leskovec. He
received his Ph.D. from MIT Physics, where his thesis focused on AI for
Physics and Physics for AI. His research interests include developing
machine learning methods for large-scale scientific simulations,
neuro-symbolic methods for scientific discovery, and representation
learning, using tools of graph neural networks, information theory, and
physics. His work has been published in top machine learning conferences
and leading physics journals, and featured in MIT Technology Review. He
also serves as a reviewer for high-impact journals such as PNAS, Nature
Communications, Nature Machine Intelligence, and Science Advances.

*In-Person seminars will be held at Mason Lab 211, 9 Hillhouse Avenue with
the option of virtual participation (*
https://yale.hosted.panopto.com/Panopto/Pages/Sessions/List.aspx?folderID=f8b73c34-a27b-42a7-a073-af2d00f90ffa
)

*3:30pm <https://0.0.0.10/> -   Pre-talk meet and greet teatime - Dana
House, 24 Hillhouse Avenue *
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.yale.edu/pipermail/sds-seminars/attachments/20230220/4de204e2/attachment.html>
-------------- next part --------------
-- 
Sds-announce mailing list
Sds-announce at mailman.yale.edu
https://mailman.yale.edu/mailman/listinfo/sds-announce