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PRODID:UW-Madison-Physics-Events
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SEQUENCE:0
UID:UW-Physics-Event-6485
DTSTART:20210714T160000Z
DTEND:20210714T171500Z
DTSTAMP:20260414T232240Z
LAST-MODIFIED:20210707T195241Z
LOCATION:Online Seminar: Please sign up for our mailing list at www.ph
 ysicsmeetsml.org for zoom link
SUMMARY:Learning Differential Equations\, Physics ∩ ML Seminar\, Jes
 se Bettencourt\, University of Toronto
DESCRIPTION:Differential equations provide a natural and productive la
 nguage to describe and manipulate physical systems. As well\, the inte
 rdisciplinary literature developed toward the study of differential eq
 uations is rich with conceptual and technical results. I will discuss 
 the integration of these methods with Machine Learning. I will introdu
 ce Neural Ordinary Differential Equations\, a class of initial value p
 roblems whose dynamics are specified by a neural network. I will descr
 ibe some methods for learning the differential equation via gradient o
 ptimization. I will highlight some areas where this treatment is both 
 conceptually elegant and practically effective. In particular\, I will
  discuss Continuous Normalizing Flows for density estimation and an ex
 tension (FFJORD) that demonstrates performance improvement through num
 erical approximation. I will also discuss recent work to regularize le
 arned differential equations such that their solution can be efficient
 ly approximated by a numerical solver. I will describe recent advances
  in (Higher-Order) Automatic Differentiation that facilitate these met
 hods and may be a useful tool for future techniques to study the inter
 face of physics and Machine Learning.
URL:https://www.physics.wisc.edu/events/?id=6485
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