BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:Europe/Stockholm
X-LIC-LOCATION:Europe/Stockholm
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:19700308T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=-1SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:19701101T020000
RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=-1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20260421T090514Z
LOCATION:Plenary Room (Bldg. 6 - 001)
DTSTART;TZID=Europe/Stockholm:20260629T194700
DTEND;TZID=Europe/Stockholm:20260629T194800
UID:submissions.pasc-conference.org_PASC26_sess124_pos102@linklings.com
SUMMARY:On the Nexus of Data, Models, and Supercomputing: Optimization and
  Uncertainty Quantification in HPC
DESCRIPTION:Antigni Georgiadou (Oak Ridge National Laboratory)\n\nThe futu
 re of HPC will blend advanced simulation with model training, integrating 
 multi-fidelity stochastic ensembles, computational steering, active learni
 ng, and interactive visualization. As we move beyond single "hero" simulat
 ions, HPC must support dynamic workflows that allow scientific questions t
 o be defined and redefined in real time. This shift demands not only high-
 resolution simulations but also the robust statistical treatment of uncert
 ainty—from propagation and calibration to assimilation of streaming data. 
 Concurrently, the advent of AI poses challenges in ensuring model trust an
 d interpretability, crucial for high-stakes applications where traditional
  PDE-based models have set the standard. With this poster we present scien
 tific results and current methodology to explore how HPC currently meets t
 hese evolving needs by balancing complex model evaluations and ensemble pr
 edictions efficiently under time and energy constraints.\n\n
END:VEVENT
END:VCALENDAR