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VERSION:2.0
PRODID:icalendar-ruby
CALSCALE:GREGORIAN
X-WR-CALNAME:Distinguished Seminar in Computational Science and Engineering
X-WR-TIMEZONE:Eastern Time (US & Canada)
BEGIN:VEVENT
DTSTAMP:20260909T103351Z
UID:tag:localist.com\,2008:EventInstance_48152852776732
DTSTART:20241205T170000Z
DTEND:20241205T180000Z
DESCRIPTION:Distinguished Seminar in Computational Science and Engineering\
 n\n \n\nDecember 5\, 2024\, 12-1PM\n\n45-432 in Building 45 and Zoom Webin
 ar\n\n \n\nLeveraging nonlinear latent dynamics of high-dimensional system
 s for data-driven predictions\nBenjamin Peherstorfer\nAssociate Professor\
 nCourant Institute of Mathematical Sciences | New York University\n\n \n\n
 Abstract: \n\nMany high-dimensional and seemingly intractable problems in 
 science and engineering have well-behaved latent dynamics that offer a pat
 h towards their solution. In this talk\, I will demonstrate that nonlinear
  approximations can help leverage latent dynamics that are out of reach of
  more traditional computational methods. First\, I will present Neural Gal
 erkin schemes that overcome the Kolmogorov barrier through nonlinear laten
 t representations and active sampling\, enabling rapid predictions of tran
 sport-dominated phenomena that are inaccessible to traditional model reduc
 tion methods. Second\, I will present a variational approach for learning 
 reduced models of systems that feature stochastic and mean-field effects. 
 The approach infers parameter- and time-dependent gradient fields to effic
 iently generate sample trajectories that approximate the system’s popula
 tion dynamics over varying physics parameters. Along the way\, I will repo
 rt numerical experiments that showcase how leveraging latent dynamics enab
 les science and engineering applications\, from modeling rotating detonati
 on waves that are of interest in space propulsion to predicting Vlasov-Poi
 sson instabilities to forecasting high-dimensional chaotic systems.\n\n \n
 \nBio:\n\nBenjamin Peherstorfer is Associate Professor at Courant Institut
 e of Mathematical Sciences. Until 2016\, he was a Postdoctoral Associate i
 n the Aerospace Computational Design Laboratory (ACDL) at the Massachusett
 s Institute of Technology (MIT)\, working with Professor Karen Willcox. He
  received B.S.\, M.S.\, and Ph.D. degrees from the Technical University of
  Munich (Germany) in 2008\, 2010\, and 2013\, respectively. His Ph.D. thes
 is was recognized with the Heinz-Schwaertzel prize\, which is jointly awar
 ded by three German universities\nto an outstanding Ph.D. thesis in comput
 er science. Benjamin was selected for a Department of Energy (DoE) Early C
 areer Award in the Applied Mathematics Program in 2018 and for an Air Forc
 e Young Investigator Program (YIP) award in Computational Mathematics in 2
 020. In 2021\, Benjamin received a National Science Foundation (NSF) CAREE
 R award in Computational Mathematics. His research focuses on computationa
 l methods for data- and compute-intensive science and engineering applicat
 ions\, including scientific machine learning\, mathematics of data science
 \, model reduction\, and computational statistics.
GEO:42.361613;-71.092293
LOCATION:Building 45 (MIT Stephen A. Schwarzman College of Computing)\, 45-
 432
SUMMARY:Distinguished Seminar in Computational Science and Engineering
URL;VALUE=URI:https://calendar.mit.edu/event/distinguished-seminar-in-compu
 tational-science-and-engineering-7190
CATEGORIES:Conferences/Seminars/Lectures
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