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SUMMARY:Nonlinear Coherence: reversing the problem - Joe Massingham\, PhD 
 student\, CUED
DTSTART:20250124T160000Z
DTEND:20250124T170000Z
UID:TALK226120@talks.cam.ac.uk
CONTACT:46601
DESCRIPTION:Predicting the response of nonlinear dynamical systems subject
  to random\, broadband excitation is important across a range of engineeri
 ng disciplines\, such as structural dynamics. Building data-driven models 
 requires experimental measurements of the system input and output\, but it
  can be difficult to determine whether inaccuracies in the model stem from
  modelling errors or noise. Therefore\, there is a need to determine the m
 aximum component of the output that could theoretically be predicted using
  the input if an improved model was to be developed through the investment
  of resources. This talk presents a novel method to identify the component
  of the output that could potentially be modelled\, and quantify the level
  of noise in the output\, as a function of frequency. The method uses inpu
 t-output measurements and an available\, but approximate\, model of the sy
 stem. A trainable\, frequency dependent parameter balances an output predi
 ction generated by the model with noisy measurements of the output to pred
 ict the input to the system. This parameter is utilised to estimate the no
 ise level and then calculate a nonlinear coherence metric as a measure of 
 causality or predictability from the input. There are currently no solutio
 ns to this problem in the absence of an accurate benchmark model.
LOCATION:JDB Seminar Room\, CUED
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