Symmetry of attractors and the Perron-Frobenius operator

Mehta, Prashant G.; Molo, Mirko Hessel-von; Dellnitz, Michael

doi:10.1080/10236190601045788

Cited by 6 publications

(2 citation statements)

References 18 publications

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“…On the transfer operator side, Mehta et al (2006) addresses symmetries of the Perron-Frobenius operator in relation to the admissible symmetry properties of attractors. Constrained Ulam DMD (Goswami et al 2018) uses a minimization problem with constraints that guarantee a positive operator with a row sum equal to one.…”

Section: Further Methodsmentioning

confidence: 99%

Residual dynamic mode decomposition: robust and verified Koopmanism

2023

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Dynamic mode decomposition (DMD) describes complex dynamic processes through a hierarchy of simpler coherent features. DMD is regularly used to understand the fundamental characteristics of turbulence and is closely related to Koopman operators. However, verifying the decomposition, equivalently the computed spectral features of Koopman operators, remains a significant challenge due to the infinite-dimensional nature of Koopman operators. Challenges include spurious (unphysical) modes and dealing with continuous spectra, which both occur regularly in turbulent flows. Residual dynamic mode decomposition (ResDMD), introduced by Colbrook & Townsend (Rigorous data-driven computation of spectral properties of Koopman operators for dynamical systems. 2021. arXiv:2111.14889), overcomes such challenges through the data-driven computation of residuals associated with the full infinite-dimensional Koopman operator. ResDMD computes spectra and pseudospectra of general Koopman operators with error control and computes smoothed approximations of spectral measures (including continuous spectra) with explicit high-order convergence theorems. ResDMD thus provides robust and verified Koopmanism. We implement ResDMD and demonstrate its application in various fluid dynamic situations at varying Reynolds numbers from both numerical and experimental data. Examples include vortex shedding behind a cylinder, hot-wire data acquired in a turbulent boundary layer, particle image velocimetry data focusing on a wall-jet flow and laser-induced plasma acoustic pressure signals. We present some advantages of ResDMD: the ability to resolve nonlinear and transient modes verifiably; the verification of learnt dictionaries; the verification of Koopman mode decompositions; and spectral calculations with reduced broadening effects. We also discuss how a new ordering of modes via residuals enables greater accuracy than the traditional modulus ordering (e.g. when forecasting) with a smaller dictionary. This result paves the way for more significant dynamic compression of large datasets without sacrificing accuracy.

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Section: Further Methodsmentioning

confidence: 99%

Residual dynamic mode decomposition: robust and verified Koopmanism

2023

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“…Stochastic methods, on the other hand, enable analysis of the asymptotic chaotic dynamics in terms of invariant measures [38]. Bifurcations in chaotic regime can be understood via analysis of the spectrum of the P-F operator or the support of the invariant measure [45]. In particular, we use these results to understand qualitative changes in queue behavior.…”

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confidence: 99%