[UCR_DataScience] REMINDER: Data Science talk by Prof. Maziar Raissi, tomorrow Friday May 10th, 12-1pm, MRB Seminar Room

[Vassilis Tsotras]

Reminder for the Data Science talk tomorrow Friday, at noon in the MRB 
seminar room.

Please use the link below to register if you plan to attend.

best,

V. Tsotras



-------- Forwarded Message --------
Subject: 	[UCR_DataScience] Data Science talk by Prof. Maziar Raissi, 
Friday May 10th, 12-1pm, MRB Seminar Room
Date: 	Tue, 7 May 2024 09:23:15 -0700
From: 	tsotras--- via DataScience <datascience at lists.ucr.edu>
Reply-To: 	tsotras at cs.ucr.edu
To: 	datascience at lists.ucr.edu



We will have a Data Science seminar this Friday, May 10th, 12:00-1:00pm at
the MRB Seminar Room (1st floor).

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

To keep track of the number of attendees, please *register* at:
https://www.eventbrite.com/e/data-science-talk-tickets-900165367847

The talk will be given by Prof. Maziar Raissi, Mathematics Department, UCR

TITLE:
"Data-Efficient Deep Learning using Physics-Informed Neural Networks"


ABSTRACT:
A grand challenge with great opportunities is to develop a coherent
framework that enables blending conservation laws, physical principles,
and/or phenomenological behaviours expressed by differential equations
with the vast data sets available in many fields of engineering, science,
and technology. At the intersection of probabilistic machine learning,
deep learning, and scientific computations, this work is pursuing the
overall vision to establish promising new directions for harnessing the
long-standing developments of classical methods in applied mathematics and
mathematical physics to design learning machines with the ability to
operate in complex domains without requiring large quantities of data. To
materialize this vision, this work is exploring two complementary
directions: (1) designing data-efficient learning machines capable of
leveraging the underlying laws of physics, expressed by time dependent and
non-linear differential equations, to extract patterns from
high-dimensional data generated from experiments, and (2) designing novel
numerical algorithms that can seamlessly blend equations and noisy
multi-fidelity data, infer latent quantities of interest (e.g., the
solution to a differential equation), and naturally quantify uncertainty
in computations.


------------------------------------
Sponsored by the UCR Data Science Center, the purpose of the Data Science
Seminars is to foster collaborations between "core" Data Science faculty
(from CSE/ECE/Stat Departments) and faculty/visitors from other sciences
that face Data Science problems in their research. These informal
gatherings are open to interested faculty and graduate students. Each
meeting will start with a talk describing research problems and then a
discussion will follow for questions, open problems, ideas for possible
collaborations etc.

A full list of previous seminars appears at:
http://datascience.ucr.edu/seminars

Forward this email to other colleagues or graduate students in your lab
that may be interested. Moreover, if you are interested in giving a Data
Science related talk, please contact me (tsotras at cs.ucr.edu).

Sincerely,
Vassilis Tsotras
Professor, CSE Department
co-Director, Data Science Major
Director, MS in Computational Data Science


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