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SUMMARY:Omri Azencot (UCLA)
DTSTART:20200701T210500Z
DTEND:20200701T213000Z
DTSTAMP:20260423T024650Z
UID:SciDL/11
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/SciDL/11/">R
 obust Prediction of High-Dimensional Dynamical Systems using Koopman Deep 
 Networks</a>\nby Omri Azencot (UCLA) as part of Workshop on Scientific-Dri
 ven Deep Learning (SciDL)\n\n\nAbstract\nWe present a new deep learning ap
 proach for the analysis and processing of time series data. At the core of
  our work is the Koopman operator which fully encodes a nonlinear dynamica
 l system. Unlike the majority of Koopman-based models\, we consider dynami
 cs for which the Koopman operator is invertible. We exploit the structure 
 of these systems to design a novel Physically-Constrained Learning (PCL) m
 odel that takes into account the inverse dynamics while penalizing for inv
 erse prediction. Our architecture is composed of an autoencoder component 
 and two Koopman layers for the dynamics and their inverse. To motivate our
  network design\, we investigate the connection between invertible Koopman
  operators and pointwise maps\, and our analysis yields a loss term which 
 we employ in practice. To evaluate our work\, we consider several challeng
 ing nonlinear systems including the pendulum\, fluid flows on curved domai
 ns and real climate data. We compare our approach to several baseline meth
 ods\, and we demonstrate that it yields the best results for long time pre
 dictions and in noisy settings.\n
LOCATION:https://researchseminars.org/talk/SciDL/11/
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