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:20250822T115808Z
LOCATION:Room 5.0B56
DTSTART;TZID=Europe/Stockholm:20250616T112000
DTEND;TZID=Europe/Stockholm:20250616T115000
UID:submissions.pasc-conference.org_PASC25_sess109_msa178@linklings.com
SUMMARY:Data-Efficient Surrogate Models for Digital Twinning
DESCRIPTION:Lorenzo Zanisi (UKAEA)\n\nNeural surrogate models of physics s
 imulators are emerging ubiquitously in the Fusion community to satisfy the
  pressing need of fast optimisation tasks and flight simulator application
 s. However, gathering the training sets for these surrogates can be very e
 xpensive, and storing the data long-term may be impossible. In this talk I
  will demonstrate methodologies to obtain performing surrogate models at a
  significantly lower cost in terms of training data, with applications to 
 0D and 2D datasets and to a reactor-relevant streaming scenario.\n\nDomain
 : Physics, Computational Methods and Applied Mathematics\n\nSession Chair:
  Laurent Villard (EPFL)\n\n
END:VEVENT
END:VCALENDAR