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X-WR-CALNAME:Neural Operator for Scientific Computing
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UID:2025-03-19-zongyi-li@cml.ics.uci.edu
DTSTAMP:20250319T000000Z
SEQUENCE:57690
DTSTART;TZID=America/Los_Angeles:20250319T110000
DTEND;TZID=America/Los_Angeles:20250319T120000
SUMMARY:[CML Seminar] Zongyi Li: Neural Operator for Scientific Computing
LOCATION:Donald Bren Hall 4011
DESCRIPTION:Zongyi Li\, PhD Student\, Computing and Mathematical Sciences\,
Caltech\n\nTitle: Neural Operator for Scientific Computing\n\nAbstract: S
cientific computing\, which aims to accurately simulate complex physical p
henomena\, often requires substantial computational resources. By viewing
data as continuous functions\, we leverage the smoothness structures of fu
nction spaces to enable efficient large-scale simulations. We introduce th
e neural operator\, a machine learning framework designed to approximate s
olution operators in infinite-dimensional spaces\, achieving scalable phys
ical simulations across diverse resolutions and geometries. Beginning with
the Fourier Neural Operator\, we explore recent advancements including sc
ale-consistent learning techniques and adaptive mesh methods. We demonstra
te the real-world impact of our framework through applications in weather
prediction\, carbon capture\, and plasma dynamics\, achieving speedups of
several orders of magnitude.\n\nhttps://cml.ics.uci.edu/seminars/2025-03-1
9-zongyi-li
X-ALT-DESC;FMTTYPE=text/html:Zongyi Li\, PhD Student\, C
omputing and Mathematical Sciences\, Caltech
Title: Neural O
perator for Scientific Computing
Abstract: Scientific comput
ing\, which aims to accurately simulate complex physical phenomena\, often
requires substantial computational resources. By viewing data as continuo
us functions\, we leverage the smoothness structures of function spaces to
enable efficient large-scale simulations. We introduce the neural operato
r\, a machine learning framework designed to approximate solution operator
s in infinite-dimensional spaces\, achieving scalable physical simulations
across diverse resolutions and geometries. Beginning with the Fourier Neu
ral Operator\, we explore recent advancements including scale-consistent l
earning techniques and adaptive mesh methods. We demonstrate the real-worl
d impact of our framework through applications in weather prediction\, car
bon capture\, and plasma dynamics\, achieving speedups of several orders o
f magnitude.
https://cml.ics.uci.edu/seminars/2025-03-19-zongyi-li
URL:https://cml.ics.uci.edu/seminars/2025-03-19-zongyi-li
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