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CATEGORIES:Statistics
SUMMARY:Physics informed spatial and functional data analy
sis over non-Euclidean domains - Laura Sangalli (P
olytechnic University of Milan)
DTSTART;TZID=Europe/London:20230519T140000
DTEND;TZID=Europe/London:20230519T150000
UID:TALK199489AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/199489
DESCRIPTION:Recent years have seen an explosive growth in the
recording of increasingly complex and high-dimensi
onal data. Classical statistical methods are often
unfit to handle such data\, whose analysis calls
for the definition of new methods merging ideas an
d approaches from statistics and applied mathemati
cs. My talk will in particular focus on spatial an
d functional data defined over non-Euclidean domai
ns\, such as linear networks\, two-dimensional man
ifolds and non-convex volumes. I will present an i
nnovative class of methods\, based on regularizing
terms involving Partial Differential Equations (P
DEs)\, defined over the complex domains being cons
idered. These physics-informed regression methods
enable the inclusion of the available problem spec
ific information\, suitably encoded in the regular
izing PDE. The proposed methods make use of advanc
ed numerical techniques\, such as finite element a
nalysis and isogeometric analisys. A challenging a
pplication to neuroimaging data will be illustrate
d.
LOCATION:MR12\, Centre for Mathematical Sciences
CONTACT:Qingyuan Zhao
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