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VERSION:2.0
PRODID:IEEE vTools.Events//EN
CALSCALE:GREGORIAN
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TZID:Pacific/Honolulu
BEGIN:STANDARD
DTSTART:19470608T023000
TZOFFSETFROM:-1130
TZOFFSETTO:-1000
TZNAME:HST
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BEGIN:VEVENT
DTSTAMP:20231202T045255Z
UID:FEF64967-0E73-4B6B-B1BC-A70B0CDAA23D
DTSTART;TZID=Pacific/Honolulu:20231130T170000
DTEND;TZID=Pacific/Honolulu:20231130T180000
DESCRIPTION:Engineers traditionally use deterministic modeling in their tas
 ks\, but challenges for developing and optimizing products and processes i
 nspire us to venture beyond deterministic to probabilistic or stochastic m
 odeling. The melding of engineering modeling with probabilistic thinking e
 mpowers engineers to develop confidence for ourselves\, our customers and 
 regulatory agencies that our products are likely to be successful and that
  we will flawlessly meet or exceed expectations over a comprehensive range
  of use conditions. Predictive engineering starts from measurable system l
 evel requirements\, exploration and documentation of use conditions\, and 
 expanding on deterministic models with probabilistic modeling using Monte 
 Carlo Simulation or Bayesian Networks to optimize the design and process. 
 Probabilistic and stochastic modeling has provided competitive advantages 
 for enterprises\, for products\, and for both experienced engineers and en
 gineers early in their careers.\n\nEvent is organized by IEEE Hawaii YP\, 
 IAS\, and WIE and co-hosted with several other organizations. Depending on
  the level of interest in this event\, a follow on series with Six Sigma c
 ertification is possible.\n\nSpeaker(s): Eric Maass\, PhD\n\nVirtual: http
 s://events.vtools.ieee.org/m/382000
LOCATION:Virtual: https://events.vtools.ieee.org/m/382000
ORGANIZER:brianne.tengan@ieee.org
SEQUENCE:20
SUMMARY:Predictive Engineering
URL;VALUE=URI:https://events.vtools.ieee.org/m/382000
X-ALT-DESC:Description: <br /><p>Engineers traditionally use deterministic 
 modeling in their tasks\, but challenges for developing and optimizing pro
 ducts and processes inspire us to venture beyond deterministic to probabil
 istic or stochastic modeling.&nbsp\; The melding of engineering modeling w
 ith probabilistic thinking empowers engineers to develop confidence for ou
 rselves\, our customers and regulatory agencies that our products are like
 ly to be successful&nbsp\; and that we will flawlessly meet or exceed expe
 ctations over a comprehensive range of use conditions. Predictive engineer
 ing starts from measurable system level requirements\, exploration and doc
 umentation of use conditions\, and expanding on deterministic models with 
 probabilistic modeling using Monte Carlo Simulation or Bayesian Networks t
 o optimize the design and process. Probabilistic and stochastic modeling h
 as provided competitive advantages for enterprises\, for products\, and fo
 r both experienced engineers and engineers early in their careers.</p>\n<p
 >Event is organized by IEEE Hawaii YP\, IAS\, and WIE and co-hosted with s
 everal other organizations. Depending on the level of interest in this eve
 nt\, a follow on series with Six Sigma certification is possible.</p>
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