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SUMMARY:Moduli-dependent Calabi-Yau and SU(3)-structure metrics from Machi
ne Learning
DTSTART;VALUE=DATE-TIME:20210218T080000Z
DTEND;VALUE=DATE-TIME:20210218T090000Z
DTSTAMP;VALUE=DATE-TIME:20210228T132035Z
UID:indico-event-405@cern.ch
DESCRIPTION:Calabi-Yau manifolds play a crucial role in string compactific
ations. Yau's theorem guarantees the existence of a metric that satisfies
the string's equation of motion. However\, Yau's proof is non-constructive
\, and no analytic expressions for metrics on Calabi-Yau threefolds are kn
own. We use machine learning\, more precisely neural networks\, to learn C
alabi-Yau metrics and their complex structure moduli dependence. I will st
art with an introduction to Calabi-Yau manifolds and their moduli. After t
hat\, I will give a brief introduction to neural networks. Using an exampl
e\, I will then illustrate how we train neural networks to find Calabi-Yau
metrics by solving the underlying partial differential equations. The app
roach generalizes to more general manifolds and can hence also be used for
manifolds with reduced structure\, such as SU(3) structure or G2 manifold
s\, which feature in string compactifications with flux and in the M-theor
y formulation of string theory\, respectively. I will illustrate this gene
ralization for a particular SU(3) structure metric and compare the machine
learning result to the known\, analytic expression.\n\nhttps://indico.ibs
.re.kr/event/405/
LOCATION:zoom meeting
URL:https://indico.ibs.re.kr/event/405/
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