Data-driven reduced order models using invariant foliations, manifolds and autoencoders
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This paper explores the question: how to identify a reduced order model from data. There are three ways to relate data to a model: invariant foliations, invariant manifolds and autoencoders. Invariant manifolds cannot be fitted to data unless a hardware in a loop system is used. Autoencoders only identify the portion of the phase space where the data is, which is not necessarily an invariant manifold. Therefore for off-line data the only option is an invariant foliation. We note that Koopman eigenfunctions also define invariant foliations, but they are limited by the assumption of linearity and resulting singularites. Finding an invariant foliation requires approximating high-dimensional functions. We propose two solutions. If an accurate reduced order model is sought, a sparse polynomial approximation is used, with polynomial coefficients that are sparse hierarchical tensors. If an invariant manifold is sought, as a leaf of a foliation, the required high-dimensional function can be approximated by a low-dimensional polynomial. The two methods can be combined to find an accurate reduced order model and an invariant manifold. We also analyse the reduced order model in case of a focus type equilibrium, typical in mechanical systems. We note that the nonlinear coordinate system defined by the invariant foliation and the invariant manifold distorts instantaneous frequencies and damping ratios, which we correct. Through examples we illustrate the calculation of invariant foliations and manifolds, and at the same time show that Koopman eigenfunctions and autoencoders fail to capture accurate reduced order models under the same conditions.
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