[ugrads] FW: [CISC Seminar 2/1] Uncertainty Quantification and Data-driven Discovery for High-dimensional Complex Systems with Multimodal Distribution

Gladys Collins collinsg at iit.edu
Tue Jan 29 09:28:58 CST 2019


*From:* Chun Liu [mailto:cliu124 at iit.edu]
*Sent:* Tuesday, January 29, 2019 9:11 AM
*To:* Gladys Collins
*Subject:* Fwd: [CISC Seminar 2/1] Uncertainty Quantification and
Data-driven Discovery for High-dimensional Complex Systems with Multimodal
Distribution




Dear Gladys,

Can you announce to the whole department?

Thanks,

Chun


*******************************************

Chun Liu

Professor and Chair

Department of Applied Mathematics

Illinois Institute of Technology

814-9549061



-------- Forwarded Message --------

*Subject: *

[CISC Seminar 2/1] Uncertainty Quantification and Data-driven Discovery for
High-dimensional Complex Systems with Multimodal Distribution

*Date: *

Wed, 23 Jan 2019 18:23:33 -0600

*From: *

Aleksei Sorokin <asorokin at hawk.iit.edu> <asorokin at hawk.iit.edu>

*To: *

CISC annoucements <cisc-annoucements-group at iit.edu>
<cisc-annoucements-group at iit.edu>



*Center for Interdisciplinary Scientific Computation Seminar Announcement*


*Speaker:* Guang Lin, Purdue University
*Date:* February 1, 2019
*Time: *2:30 pm
*Location: * John T. Rettaliata Engineering Center, Room 103
*Title: *Uncertainty Quantification and Data-driven Discovery for
High-dimensional Complex Systems with Multimodal Distribution

*Abstract:*
Experience suggests that uncertainties often play an important role in
quantifying the performance of complex systems. Therefore, uncertainty
needs to be treated as a core element in the modeling, simulation, and
optimization of complex systems. The field of uncertainty quantification
(UQ) has received an increasing amount of attention. Extensive research
efforts have been devoted to it and many novel numerical techniques have
been developed. These techniques aim to conduct stochastic simulations for
very large-scale complex systems.


In this talk, we will present some effective new ways of dealing with the
challenges facing uncertainty quantification community including
high-dimensionality, discontinuities, “multi-modal”, model-form
uncertainties, UQ for computational-expensive models, UQ for machine
learning and data science, etc.


Particularly, a rotation-based compressive sensing technique is developed
for high-dimensional UQ problem. Adaptive importance sampling techniques
will be discussed for handling multi-modal problems. We demonstrate that we
can use emerging, large-scale spatiotemporal data from modern sensors to
directly construct and discover, in an adaptive manner, governing
equations, even nonlinear dynamics, that best model the system and
quantifying the uncertainties in the learning process. Several specific
examples of flow and transport in randomly heterogeneous porous media and
climate models will be presented to illustrate the main idea of our
approaches.

*Speaker Bio:*
Guang Lin is the Director of Purdue Data Science Consulting Services,
Associate Professor in the Department of Mathematics, Department of
Statistics, and School of Mechanical Engineering at Purdue University. He
was awarded the B.S. in Mechanics from Zhejiang University in 1997 and the
Ph.D. in Applied Mathematics from Brown University in 2007. He worked as a
staff scientist at Pacific Northwest National Laboratory (2007-2014) before
he joined the faculty of Mathematics and Mechanical Engineering in 2014.

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