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SUMMARY:Machine Learning and String theory - Challenger Mishra
DTSTART:20210223T131500Z
DTEND:20210223T141500Z
UID:TALK155047@talks.cam.ac.uk
CONTACT:Mateja Jamnik
DESCRIPTION:"Join us on Zoom":https://zoom.us/j/99166955895?pwd=SzI0M3pMVE
 kvNmw3Q0dqNDVRalZvdz09\n\nOne of the holy grails of modern theoretical phy
 sics is the unification of Quantum Mechanics with Einstein’s relativity.
  String theory is the only known consistent theory of quantum gravity\, an
 d arguably the most promising candidate for a unified theory of physics. S
 ince its inception in the late 1960s\, it has provided tremendous insights
  into our understanding of the physical world\, and has overseen many inte
 resting developments in various branches of pure mathematics and theoretic
 al physics. Despite string theory’s many successes\, a string model that
  explains all observed data from cosmology and particle physics experiment
 s\, has eluded discovery. This is owing to the particularly large landscap
 e of valid string theory solutions\, estimated to be of the size 10^{270\,
 000}. Most of these solutions are thought to lead to descriptions of unive
 rses that do not resemble ours in detail. \n\nString theory posits extra-d
 imensions of space. These are often described by complex geometries called
  Calabi--Yau manifolds. In this talk\, I will describe recent progress in 
 utilising machine learning in modelling topological and geometric properti
 es of such manifolds\, and showcase how it brings us closer to understandi
 ng quantum gravity with the help of machine learning. I will also describe
  a second example of how simple tools from machine learning can be used to
  predict properties of hadronic matter in the realm of Quantum Chromodynam
 ics. 
LOCATION:Zoom
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