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:20250822T115807Z
LOCATION:Room 5.0B15 & 16
DTSTART;TZID=Europe/Stockholm:20250616T170000
DTEND;TZID=Europe/Stockholm:20250616T173000
UID:submissions.pasc-conference.org_PASC25_sess167_pap111@linklings.com
SUMMARY:Space-Time Parallel Scaling of Parareal with a Physics-Informed Fo
 urier Neural Operator Coarse Propagator Applied to the Black-Scholes Equat
 ion
DESCRIPTION:Abdul Qadir Ibrahim, Sebastian Götschel, and Daniel Ruprecht (
 Hamburg University of Technology)\n\nIterative parallel-in-time algorithms
  like Parareal can extend scaling beyond the saturation of purely spatial 
 parallelization when solving initial value problems. However, they require
  the user to build coarse models to handle the unavoidable serial transpor
 t of information in time. This is a time-consuming and difficult process s
 ince there is still limited theoretical insight into what constitutes a go
 od and efficient coarse model. Novel approaches from machine learning to s
 olve differential equations could provide a more generic way to find coars
 e-level models for parallel-in-time algorithms.<br /> This paper demonstra
 tes that a physics-informed Fourier Neural Operator (PINO) is an effective
  coarse model for the parallelization in time of the two-asset Black-Schol
 es equation using Parareal. We demonstrate that PINO-Parareal converges as
  fast as a bespoke numerical coarse model and that, in combination with sp
 atial parallelization by domain decomposition, it provides better overall 
 speedup than both purely spatial parallelization and space-time paralleliz
 ation with a numerical coarse propagator.\n\nDomain: Climate, Weather, and
  Earth Sciences, Computational Methods and Applied Mathematics\n\nSession 
 Chair: Fawzi Mohamed (ETH Zurich / CSCS)\n\n
END:VEVENT
END:VCALENDAR