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VERSION:2.0
PRODID:icalendar-ruby
CALSCALE:GREGORIAN
X-WR-CALNAME:Physical Mathematics Seminar
X-WR-TIMEZONE:Eastern Time (US & Canada)
BEGIN:VEVENT
DTSTAMP:20260518T091442Z
UID:tag:localist.com\,2008:EventInstance_42555801056734
DTSTART:20230404T183000Z
DTEND:20230404T193000Z
DESCRIPTION:Speaker:  Niall Mangan  (Northwestern University)\n\nTitle:  Mo
 del selection chaotic systems from data with hidden variables using sparse
  data assimilation\n\nAbstract:\n\nMany natural systems exhibit chaotic be
 havior\, including the weather\, hydrology\, neuroscience\, and population
  dynamics. Although many chaotic systems can be described by relatively si
 mple dynamical equations\, characterizing these systems can be challenging
  due to sensitivity to initial conditions and difficulties in differentiat
 ing chaotic behavior from noise. Ideally\, one wishes to find a parsimonio
 us set of equations that describe a dynamical system. However\, model sele
 ction is more challenging when only a subset of the variables are experime
 ntally accessible. Manifold learning methods using time-delay embeddings c
 an successfully reconstruct the underlying structure of the system from da
 ta with hidden variables\, but not the equations. Recent work in sparse-op
 timization based model selection has enabled model discovery given a libra
 ry of possible terms\, but regression-based methods require measurements o
 f all state variables. We present a method combining variational annealing
 —a technique previously used for parameter estimation in chaotic systems
  with hidden variables—with sparse-optimization methods to perform model
  identification for chaotic systems with unmeasured variables. We applied 
 the method to ground-truth time-series simulated from the classic Lorenz s
 ystem and experimental data from an electrical circuit with Lorenz-system 
 like behavior. In both cases\, we successfully recover the expected equati
 ons with two measured and one hidden variable. Application to simulated da
 ta from the Colpitts oscillator demonstrates successful model selection of
  terms within nonlinear functions. This work was recently published: Chaos
  32\, 063101(2022)\; https://doi.org/10.1063/5.0066066
LOCATION:MIT-Math Dept.\, Room 2-449
SUMMARY:Physical Mathematics Seminar
URL;VALUE=URI:https://calendar.mit.edu/event/physical_mathematics_seminar_1
 096
CATEGORIES:Conferences/Seminars/Lectures
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