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DTSTAMP:20260411T234231
DTSTART:20171002T113000
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URL:https://murmitoyen.com/events/vanille/udem/detail/781714-applying-deep-
 learning-and-artificial-intelligence-to-problems-in-physics-isaac-tamblyn-
 nrc
LOCATION:Université de Montréal - Pavillon J.-Armand-Bombardier\, 5155\, 
 chemin de la rampe \, Montréal\, QC\, Canada\, H3T 2B2
SUMMARY:Applying deep learning and artificial intelligence to problems in p
 hysics - Isaac Tamblyn (NRC)
DESCRIPTION:Applying deep learning and artificial intelligence to problems 
 in physics Isaac TamblynComputational Laboratory for Energy And Nanoscienc
 eNational Research Council of Canada
Abstract: Within the past decade\, th
 e fields of artificial intelligence\, computer vision\, and natural langua
 ge processing have advanced at unprecedented rates. Computerized identific
 ation and classification of images\, video\, audio\, and written text have
  all improved to the extent they are now part of everyday technologies suc
 h as digital voice assistants (e.g. Siri)\, automated banking machines (re
 ading handwritten cheques)\, and driving assist vehicles (automatic lane c
 hange\, self-parking\, and anticipative braking). Many of the major recent
  breakthroughs have been enabled by a single class of approaches: deep neu
 ral networks (DNN). DNN are an extension of work on artificial neural netw
 orks initiated during the 1970’s-1980’s. Deep networks make use of man
 y layers of perceptrons with specialized connectivities and functions\, wh
 ere layers within the model are able to automatically learn important feat
 ures present within a dataset.
Deep learning represents a new frontier for
  computational physics. I will discuss our recent work with DNN as applied
  to simple classical\, quantum mechanical\, and atomistic problems. Result
 s for applying generative adversarial networks and reinforcement learning 
 to the inverse design and control problem will also be discussed.
Bio: Isa
 ac Tamblyn is a Research Officer at the National Research Council of Canad
 a. His group (http://clean.energyscience.ca) applies machine learning and 
 artificial intelligence to problems in computational nanoscience. He holds
  Adjunct status in the Physics Departments of uOttawa and UOIT. He complet
 ed his PhD in Physics at Dalhousie\, and held postdoctoral fellowships at 
 Berkeley Lab and Lawrence Livermore National Laboratory.
For further infor
 mation\, you may consult the web page: http://clean.energyscience.ca
Cette
  conférence est présentée par le RQMP Versant Nord du Département d
 e physique de l'Université de Montréal et de Génie physique de la Po
 lytechnique.
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