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UID:0-6587@eng.ufl.edu
DTSTART;TZID=America/New_York:20240322T104000
DTEND;TZID=America/New_York:20240322T113000
DTSTAMP:20251201T141135Z
URL:https://www.eng.ufl.edu/news-events/events/ise-seminar-machine-learnin
 g-and-computer-simulation-digital-twin-for-stochastic-decision-making/
SUMMARY:ISE Seminar "Machine Learning and Computer Simulation (Digital Twin
 ) for Stochastic Decision Making"
DESCRIPTION:ISE Seminar "Machine Learning and Computer Simulation (Digital 
 Twin) for Stochastic Decision Making"\nChun-Hung Chen\nDept. of Systems En
 gineering &amp\; Operations Research\nGeorge Mason University\, USA\nABSTR
 ACT\nWhile machine learning enjoys widespread popularity\, its efficacy re
 lies on the quality of accessible data and the depth of training\, thereby
  constraining its ability to swiftly respond to unforeseen events. Transfe
 r learning\, by retaining knowledge within pre-trained models\, accelerate
 s adaptation to new tasks using smaller datasets and fewer resources. Neve
 rtheless\, its success is still contingent upon the data and training. In 
 contrast\, computer simulation (or digital twin) offers a remedy for scena
 rios where data are sparse or events remain unobserved\, though it entails
  computational expenses\, especially in the pursuit of optimal decision-ma
 king. In our presentation\, we will introduce an innovative integration of
  computer simulation and machine learning designed to efficiently identify
  optimal decisions\, while effectively tackling challenges related to data
  availability\, training constraints\, and unpredictability of future even
 ts. A key component of our methodologies is a popular technique called Opt
 imal Computing Budget Allocation (OCBA) originally pioneered by the speake
 r\, which aims to maximize the efficiency of simulation optimization.\nZoo
 m link for the seminar: https://ufl.zoom.us/j/94548030552
CATEGORIES:Seminars
LOCATION:Weil 406\, 1949 Stadium Dr\, Gainesville\, FL\, 32611\, United Sta
 tes
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