[AI Seminar Series] Seminar by Prof. Nathan Kutz, UofWashington, THURSDAY April 17, 11am, Winston Chung Hall 205-206

[Vassilis Tsotras]

Dear colleagues,
the next AI Seminar will be on THURSDAY April 17, at 11am in Winston 
Chung Hall(WCH) 205-206.
**Please note the day, time and location change**!!!
This seminar is in collaboration with the Department of Mechanical 
Engineering.

**** Pizza and refreshments will be provided ****

To keep track of the number of attendees, please *register* at:
https://www.eventbrite.com/e/ai-seminar-series-tickets-1321906661409


The talk will be given by by Prof. Nathan Kutz, Applied Mathematics and 
Electrical and Computer Engineering, Director of the AI Institute in 
Dynamic Systems, University of Washington


TITLE:
"Learning Physics from Videos"


ABSTRACT:
Sensing is a universal task in science and engineering. Downstream tasks 
from sensing include learning dynamical models, inferring full state 
estimates of a system (system identification), control decisions, and 
forecasting. These tasks are exceptionally challenging to achieve with 
limited sensors, noisy measurements, and corrupt or missing data. 
Existing techniques typically use current (static) sensor measurements 
to perform such tasks and require principled sensor placement or an 
abundance of randomly placed sensors. In contrast, we propose a SHallow 
REcurrent Decoder (SHRED) neural network structure which incorporates 
(i) a recurrent neural network (LSTM) to learn a latent representation 
of the temporal dynamics of the sensors, and (ii) a shallow decoder that 
learns a mapping between this latent representation and the 
high-dimensional state space. By explicitly accounting for the 
time-history, or trajectory, of the sensor measurements, SHRED enables 
accurate reconstructions with far fewer sensors, outperforms existing 
techniques when more measurements are available, and is agnostic towards 
sensor placement. In addition, a compressed representation of the 
high-dimensional state is directly obtained from sensor measurements, 
which provides an on-the-fly compression for modeling physical and 
engineering systems. Forecasting is also achieved from the sensor 
time-series data alone, producing an efficient paradigm for predicting 
temporal evolution with an exceptionally limited number of sensors. In 
the example cases explored, including turbulent flows, complex 
spatio-temporal dynamics can be characterized with exceedingly limited 
sensors that can be randomly placed with minimal loss of performance.



Bio:

Nathan Kutz is the Boeing Professor of AI and Data-Driven Engineering in 
the Department of Applied Mathematics and Electrical and Computer 
Engineering and Director of the AI Institute in Dynamic Systems at the 
University of Washington, having served as chair of applied mathematics 
from 2007-2015. He received the BS degree in physics and mathematics 
from the University of Washington in 1990 and the Phd in applied 
mathematics from Northwestern University in 1994. He was a postdoc in 
the applied and computational mathematics program at Princeton 
University before taking his faculty position. He has a wide range of 
interests, including neuroscience to fluid dynamics where he integrates 
machine learning with dynamical systems and control.


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Sponsored by the RAISE at UCR Institute, the AI Seminar Series presents 
speakers working on cutting edge Foundational AI or apply AI in their 
research. The goal of these seminars is to inform the UCR community 
about current trends in AI research and promote collaborations between 
faculty in this emerging field.
These seminars are open to interested faculty and graduate students. 
Please forward this email to other colleagues or graduate students in 
your lab that may be interested. After the seminar a discussion will 
follow for questions, open problems, ideas for possible collaborations 
etc.

Sincerely,
Vassilis Tsotras
Professor, CSE Department
co-Director, RAISE at UCR Institute

Amit Roy-Chowdhury
Professor, ECE Department
co-Director, RAISE at UCR Institute
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