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

[Vassilis Tsotras]

This is a reminder for the AI Seminar (in collaboration with the 
Mechanical Engineering Dept) this week.
It will happen on THURSDAY, at 11am in WCH 205-206 (note the different 
day, time and place than usual).
The speaker is Prof. Kutz, the Director of the AI Institute in Dynamic 
Systems at UofWashington.
Please register below if you plan to attend.

Sincerely,
V. Tsotras

-----------------------

On 2025-04-13 17:47, Vassilis Tsotras wrote:
> 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.
> 
> 
> ------------------------------------
> 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
> --