Poltergeist is a package for quick, accurate and abstract approximation of statistical properties of one-dimensional chaotic dynamical systems. It treats chaotic systems through the framework of spectral methods (i.e. transfer operator-based approaches to dynamical systems) and is numerically implemented via spectral methods (e.g. Fourier and Chebyshev). For the latter, Poltergeist relies on and closely interfaces with the adaptive function approximation package ApproxFun. The use of highly accurate Fourier and Chebyshev approximations means spectrally fast convergence: one can calculate acims to 15 digits of accuracy in a fraction of a second
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References in zbMATH (referenced in 2 articles )
Showing results 1 to 2 of 2.
- Wormell, Caroline: Spectral Galerkin methods for transfer operators in uniformly expanding dynamics (2019)
- Wormell, Caroline L.; Gottwald, Georg A.: Linear response for macroscopic observables in high-dimensional systems (2019)