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:20260629T193100
DTEND;TZID=Europe/Stockholm:20260629T193200
UID:submissions.pasc-conference.org_PASC26_sess124_pos114@linklings.com
SUMMARY:Estimation of Global Surface Carbon Fluxes at the Grid Scale Using
  Machine Learning Techniques
DESCRIPTION:Ji-Sun Kang (Korea Institute of Science and Technology Informa
 tion)\n\nMachine learning (ML) techniques have recently been applied in th
 e field of geoscience as in other fields, and has shown significant progre
 ss. One of the major advantages of ML is its remarkable effectiveness in o
 vercoming the problem of realistic computational costs from a computationa
 l science perspective. This study applies ML techniques to inverse modelin
 g for estimating global carbon dioxide emissions, to see how easily and ac
 curately ML techniques could perform the calculations. With the experience
  using data assimilation techniques based on ensemble Kalman filters, the 
 differences in methodology and the associated effort can be appreciated. I
 n the meantime, applying ML techniques is also essential given recent chan
 ges in HPC architecture. KISTI (Korea Institute of Science and Technology 
 Information) national supercomputing center is building the KISTI-6 HPC sy
 stem with a performance of approximately 600 PF of which 588.28 PF will be
  from GPUs, aiming for official service in the send half of this year. The
 refore, experimenting with whether GPU-based ML model can efficiently prod
 uce similar or more accurate results than existing CPU-based numerical mod
 el-based inversion modeling is also meaningful in terms of enhancing suppo
 rt capabilities for future users of KISTI’s supercomputing service, especi
 ally those in the geosciences field.\n\n
END:VEVENT
END:VCALENDAR