BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:America/Chicago
X-LIC-LOCATION:America/Chicago
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:19700308T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:19701101T020000
RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20260202T201803Z
LOCATION:241
DTSTART;TZID=America/Chicago:20251116T090000
DTEND;TZID=America/Chicago:20251116T090100
UID:submissions.supercomputing.org_SC25_sess209_wksp125@linklings.com
SUMMARY:Frontiers in Generative AI for HPC Science and Engineering: Founda
 tions, Challenges, and Opportunities
DESCRIPTION:Charlie Catlett (Argonne National Laboratory (ANL), University
  of Chicago); Fabrizio Gagliardi (Barcelona Supercomputing Center (BSC)); 
 Valerie Taylor (Argonne National Laboratory (ANL), Univeristy of Chicago);
  Laura Morselli (CINECA); and Rio Yokota (Institute of Science Tokyo)\n\nR
 ealizing the promise of large-scale foundation models for scientific disco
 very—enabling self-driving laboratories, hypothesis generation, and more—r
 equires unprecedented computational scale and multidisciplinary efforts to
  prepare diverse scientific data. While only a few organizations can train
  state-of-the-art models from scratch (e.g., trillions of parameters, tens
  of trillions of tokens), advances in training strategies and fine-tuning 
 have expanded accessibility. Simultaneously, breakthroughs in training met
 hodologies and data quality are dramatically reducing training costs and i
 mproving the performance of even smaller AI models. As AI models advance i
 n general-purpose tasks, the scientific community is refining methods to e
 valuate and enhance their scientific reasoning capabilities, a critical ch
 allenge for trustworthy AI in science. This workshop, catalyzed by the Tri
 llion Parameter Consortium (TPC), will highlight collaborations in scienti
 fic skills evaluation, performance optimization, federated learning, respo
 nsible AI, and other topics. SC24 drew 33 submissions, with 13 presented t
 o nearly 200 attendees, underscoring the rapid evolution of this field.\n\
 nRecording: Livestreamed, Recorded\n\nRegistration Category: Technical Pro
 gram Reg Pass, Workshop Reg Pass\n\nSession Chairs: Charlie Catlett (Argon
 ne National Laboratory (ANL), University of Chicago); Javier Aula-Blasco (
 Barcelona Supercomputing Center (BSC)); Valerie Taylor (Argonne National L
 aboratory (ANL), University of Chicago); Laura Morselli (CINECA); and Rio 
 Yokota (Institute of Science Tokyo)\n\n
END:VEVENT
END:VCALENDAR