Search Results for author: Christopher W. Curtis

Found 2 papers, 1 papers with code

Machine Learning Enhanced Hankel Dynamic-Mode Decomposition

no code implementations11 Mar 2023 Christopher W. Curtis, D. Jay Alford-Lago, Erik Bollt, Andrew Tuma

This appears to be a key feature in enhancing the DMD overall, and it should help provide further insight for developing other deep learning methods for time series analysis and model generation.

Time Series Time Series Forecasting

Deep Learning Enhanced Dynamic Mode Decomposition

1 code implementation10 Aug 2021 Daniel J. Alford-Lago, Christopher W. Curtis, Alexander T. Ihler, Opal Issan

Koopman operator theory shows how nonlinear dynamical systems can be represented as an infinite-dimensional, linear operator acting on a Hilbert space of observables of the system.

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