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Numerical continuation in nonlinear experiments using local Gaussian process regression

L. Renson, J. Sieber, D. A. W. Barton, Alexander Shaw Orcid Logo, S. A. Neild

Nonlinear Dynamics, Volume: 98, Issue: 4, Pages: 2811 - 2826

Swansea University Author: Alexander Shaw Orcid Logo

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DOI (Published version): 10.1007/s11071-019-05118-y

Abstract

Control-based continuation (CBC) is a general and systematic method to probe the dynamics of nonlinear experiments. In this paper, CBC is combined with a novel continuation algorithm that is robust to experimental noise and enables the tracking of geometric features of the response surface such as f...

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Published in: Nonlinear Dynamics
ISSN: 0924-090X 1573-269X
Published: Springer Science and Business Media LLC 2019
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URI: https://cronfa.swan.ac.uk/Record/cronfa51462
Abstract: Control-based continuation (CBC) is a general and systematic method to probe the dynamics of nonlinear experiments. In this paper, CBC is combined with a novel continuation algorithm that is robust to experimental noise and enables the tracking of geometric features of the response surface such as folds. The method uses Gaussian process regression to create a local model of the response surface on which standard numerical continuation algorithms can be applied. The local model evolves as continuation explores the experimental parameter space, exploiting previously captured data to actively select the next data points to collect such that they maximise the potential information gain about the feature of interest. The method is demonstrated experimentally on a nonlinear structure featuring harmonically coupled modes. Fold points present in the response surface of the system are followed and reveal the presence of an isola, i.e. a branch of periodic responses detached from the main resonance peak.
Keywords: Nonlinear experiment, Control-based continuation, Regression-based continuation, Gaussian process regression, Active data selection
College: Faculty of Science and Engineering
Issue: 4
Start Page: 2811
End Page: 2826

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