[Forum SIS] Webinar Remind & Link - MHY TAN -City Univ. Hong Kong

[Rossella Berni]

 Dear All,
in what follows a reminder of the following StEering Center  webinar (*link
below*):

Speaker:  *Prof Matthias H.Y. Tan*,
School of Data Science, City University of Hong Kong
*Title*:  Bayesian Optimization of Expected Quadratic Loss for
Multiresponse Computer Experiments with Internal Noise.
Looking forward to your partecipation,
Thanks and Best Regards
Rossella Berni

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StEering Webinar - Prof. MHY TAN
Next Monday, May 10th · 10:30AM – 12:30PM
Please find below the link to Google Meet
: https://meet.google.com/gdf-rgmb-fpd
‪(US) +1 347-486-6739‬ PIN: ‪951 651 368‬#

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*Abstract*: Design of systems based on computer simulations is prevalent.
An important idea to improve design quality, called robust parameter design
(RPD), is to optimize control factors based on the expectation of a loss
function so that the design is robust to noise factor variations. When
computer simulations are time consuming, optimizing the simulator based on
a Gaussian process (GP) emulator for the response is a computationally
efficient approach. For this purpose, acquisition functions (AFs) are used
to sequentially determine the next design point so that the GP emulator can
more accurately locate the optimal setting.
Despite this, few articles consider AFs for positive definite quadratic
forms such as the expected quadratic loss (EQL) function, which is the
standard expected loss function for RPD with nominally-the-best responses.
This paper proposes new AFs for optimizing the EQL, analyzes their
convergence, and develops quick and accurate methods based on the
characteristic function of the EQL to compute them. We apply the AFs to RPD
problems with internal noise factors based on a GP model and an initial
design tailored for such problems. Numerical results indicate that all four
AFs considered have similar performance, and they outperform an
optimization approach based on modeling the quadratic loss as a GP and
maximin Latin hypercube designs.

*Short Biography*: Matthias Hwai Yong Tan is an associate professor in the
School of Data Science at City University of Hong Kong. He received his
B.Eng. degree in Mechanical-Industrial Engineering from the Universiti
Teknologi Malaysia, an M.Eng. degree in Industrial and Systems Engineering
from the National University of Singapore and a Ph.D. degree in Industrial
and Systems Engineering from Georgia Institute of Technology. His research
interests include uncertainty quantification, design and analysis of
computer experiments, and applied statistics.



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*____________________________________________________________*

* Rossella Berni   PhD*


*Professor of Economic Statistics*

https://www.disia.unifi.it/p-doc2-2017-200*052-B-3f2a3d2f33292d-0.html*
<https://www.disia.unifi.it/p-doc2-2017-200052-B-3f2a3d2f33292d-0.html>
<http://goog_547437445>

http://local.disia.unifi.it/berni


*Centro di Ricerca Interuniversitario - StEering: design, quality and
reliability*

https://www.disia.unifi.it/p186.html
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