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DTSTAMP:20260404T151052Z
UID:1588600800@ist.ac.at
DTSTART:20200504T160000
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DESCRIPTION:Speaker: Andrea Liu\nhosted by Scott Waitukaitis\nAbstract: Thi
 s Institute Colloquium will take place as a webinar.  All registered part
 icipants will receive a link to join the webinar.  In order to register\
 , please fill out the registration form (https://istaustria.microsoftcrmpo
 rtals.com/event/registration?id=Institute_Colloquium1578427004).Big data m
 ethods such as machine learning are finding increasing applications in phy
 sics. Machine learning is now commonly used as a classification tool for d
 istinguishing astrophysical features from observational data\, to construc
 t triggers in high energy experiments\, to identify biological components 
 such as cells in tissues\, etc.. It is also used as an approximation tool 
 for electronic structure calculations or solution of partial differential 
 equations. However\, machine learning can also be used to gain new concept
 ual understanding in physics. I will discuss the problems of dynamics and 
 plasticity in glassy systems\, which involve nonlinear responses in system
 s that are far from equilibrium and disordered\, and therefore resistant t
 o traditional statistical mechanics approaches. We have used machine learn
 ing to analyze microscopic data from simulations and experiments to identi
 fy a structural quantity that is highly correlated with dynamics in glassy
  systems\, an identification that eluded physicists for more than 50 years
 . This quantity\, “softness\,” considerably simplifies our understandi
 ng of glassy dynamics and plasticity.
LOCATION:Webinar\, ISTA
ORGANIZER:arinya.eller@ist.ac.at
SUMMARY:Andrea Liu: [Webinar] Learning physics from machine learning
URL:https://talks-calendar.ista.ac.at/events/1160
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