piDMD learns a single parameter-affine Koopman surrogate ROM from training samples at multiple parameters to predict dynamics at unseen parameters with improved robustness over interpolation baselines.
arXiv (2014)
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Nowhere-vanishing Koopman eigenfunctions form a multiplicative group, enabling polynomial extensions from principal ones to enrich eigenspaces and enable global representations from local data in multistable systems.
A hybrid estimation framework combines simplified reference dynamics with a data-driven surrogate sensor model to reconstruct system states from incomplete measurements.
citing papers explorer
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Parametric Interpolation of Dynamic Mode Decomposition for Predicting Nonlinear Systems
piDMD learns a single parameter-affine Koopman surrogate ROM from training samples at multiple parameters to predict dynamics at unseen parameters with improved robustness over interpolation baselines.
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On the algebra of Koopman eigenfunctions and on some of their infinities
Nowhere-vanishing Koopman eigenfunctions form a multiplicative group, enabling polynomial extensions from principal ones to enrich eigenspaces and enable global representations from local data in multistable systems.
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State Forecasting in an Estimation Framework with Surrogate Sensor Modeling
A hybrid estimation framework combines simplified reference dynamics with a data-driven surrogate sensor model to reconstruct system states from incomplete measurements.