Measure-preserving EDMD: A 4-line structure-preserving & convergent DMD algorithm!

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Research Abstract by Matt Colbrook, Cambridge University

We introduce measure-preserving extended dynamic mode decomposition (mpEDMD), a data-driven algorithm that enforces measure-preserving truncations of Koopman operators using a general dictionary of observables. It is flexible and easy to use with any pre-existing DMD-type method and with different data types. As well as convergence to the spectral properties of the underlying Koopman operator (for general measure-preserving dynamical systems), mpEDMD has improved stability and qualitative behavior of trajectories. For delay embedding, mpEDMD even comes with explicit convergence rates as the size of the dictionary increases. We demonstrate mpEDMD on a range of challenging examples, its increased robustness to noise compared with other DMD-type methods, and its ability to capture the energy conservation and statistics of a turbulent boundary layer flow with Reynolds number greater than 60,000 and state-space dimension greater than 100,000.

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I'm a little confused about the quadrature weights w_i. Are you selecting the node points, then using w_i from the appropriate quadrature rule formula, or using evenly spaced node points? I don't get the opportunity to select nodes in my field, so selecting, e.g, Tchebyshev nodes is not possible with my data.

scottmiller
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can anyone provide me with the code of mpEDMD algorithm
I need it for generating dictionary functions and koopman eigen values and eigen vectors

AmanThakur-vu
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This was a good presentation on its own, but the entire first half duplicated the Koopman and extended DMD material from the previous Residual DMD lecture. I suggest in the future to have a separate lecture on those topics that can be referred to, which would allow the presenter to spend more time on the actual topic and new developments.

johnathancorgan
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I made an algorithm shown in my videos called Fast Equation Solving Algorithm, that might be useful.

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