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SUMMARY:AI for physics &amp\; physics for AI - Prof. Max Tegmark (MIT)
DTSTART:20210126T160000Z
DTEND:20210126T170000Z
UID:TALK154387@talks.cam.ac.uk
CONTACT:William Fawcett
DESCRIPTION:A central goal of physics is to discover mathematical patterns
  in data. For example\, after four years of analyzing data tables on plane
 tary orbits\, Johannes Kepler started a scientific revolution in 1605 by d
 iscovering that Mars' orbit was an ellipse. I describe how we can automate
  such tasks with machine learning and not only discover symbolic formulas 
 accurately matching datasets (so-called symbolic regression)\, equations o
 f motion and conserved quantities\, but also auto-discover which degrees o
 f freedom are most useful for predicting time evolution (for example\, opt
 imal generalized coordinates extracted from video data). The methods I pre
 sent exploit numerous ideas from physics to recursively simplify neural ne
 tworks\, ranging from symmetries to differentiable manifolds\, curvature a
 nd topological defects\, and also take advantage of mathematical insights 
 from knot theory and graph modularity.
LOCATION:https://cern.zoom.us/j/61144924828?pwd=WDB6QmFCMEZiSEN5Q0k2aklWSj
 k5Zz09
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