BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:Generating new physics models from machine learning - Sven Krippen
 dorf\, LMU
DTSTART:20181121T141500Z
DTEND:20181121T151500Z
UID:TALK111517@talks.cam.ac.uk
CONTACT:Francesca Chadha-Day
DESCRIPTION:I will briefly discuss where we can expect machine learning to
  impact long standing problems in fundamental theoretical physics (e.g. fi
 nding testable predictions from string theory) and why it seems reasonable
  to start addressing these questions now. To illustrate the potential impa
 ct machine learning techniques can have\, I focus on our recent example (a
 rXiv:1809.02612)\, where we automatise the construction of physical models
 \, satisfying both experimental and theoretical constraints. I present a f
 ramework which allows the generation of effective field theories using Gen
 erative Adversarial Networks. We identify consistent examples generated by
  the machine which fall outside the class of data used for training. As a 
 starting point\, we apply this idea to the generation of supersymmetric fi
 eld theories. In this case\, the machine knows consistent examples of supe
 rsymmetric field theories with a single field and generates new examples o
 f such theories. In the generated potentials we find distinct properties\,
  e.g. the number of minima in the scalar potential\, with values not found
  in the training data. I will comment on further applications of this fram
 ework in string theory and fundamental physics.
LOCATION:MR2\, Centre for Mathematical Sciences
END:VEVENT
END:VCALENDAR