Normalising Flows and Nonlinear Normal Modes

نویسندگان

چکیده

In the context of dynamic decoupling problems, engineering dynamics has long held modal analysis as an exemplar. The method allows exact decomposition linear multi-degree-of-freedom (MDOF) systems into single-degree-of-freedom (SDOF) oscillators, thus simplifying complex problems considerably. However, is very much a theory; if applied to nonlinear systems, property (among others) lost. This unfortunate situation led numerous attempts formulate workable versions theory. current paper extends previous work by authors in using machine learning methods learn transformations on measured data, based premise that any latent variables should be statistically independent. Unlike work, transformation here exploits recent development normalising flows constructing required transformations. new approach shown overcome number original when demonstrated simulated system.

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ژورنال

عنوان ژورنال: IFAC-PapersOnLine

سال: 2021

ISSN: ['2405-8963', '2405-8971']

DOI: https://doi.org/10.1016/j.ifacol.2021.08.435