BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:A weak convergence viewpoint on invertible coarse-graining - Prof.
  Grant M. Rotskoff\, Stanford
DTSTART:20221128T143000Z
DTEND:20221128T150000Z
UID:TALK193360@talks.cam.ac.uk
CONTACT:Dr Christoph Schran
DESCRIPTION:In probability theory\, the notion of "weak convergence" is of
 ten used to describe two equivalent probability distributions. This relaxe
 d metric requires equivalence of the average value of any function under t
 he two probability distributions being compared. In coarse-graining\, Noid
  and Voth developed a thermodynamic equivalence principle that has a simil
 ar requirement. Nevertheless\, there are many functions of the fine-graine
 d system that we simply cannot evaluate on the coarse-grained degrees of f
 reedom. In this talk\, I will describe an approach that combines force-mat
 ching based coarse-graining with invertible neural networks to invert a co
 arse-graining map in a statistically precise fashion. I will show that for
  non-trivial biomolecular systems\, we can recover the fine-grained free e
 nergy surface from coarse-grained sampling.
LOCATION:Zoom link: https://zoom.us/j/92447982065?pwd=RkhaYkM5VTZPZ3pYSHpt
 UXlRSkppQT09
END:VEVENT
END:VCALENDAR