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Found 7 papers, 3 papers with code
Koopman Learning with Episodic Memory
We find that a basic implementation of Koopman learning with episodic memory leads to significant improvements in prediction on synthetic and real-world data.
On Equivalent Optimization of Machine Learning Methods
Our data-driven approach is general and can be utilized broadly to compare the optimization of machine learning methods.
Predicting the Critical Number of Layers for Hierarchical Support Vector Regression
Hierarchical support vector regression (HSVR) models a function from data as a linear combination of SVR models at a range of scales, starting at a coarse scale and moving to finer scales as the hierarchy continues.
Applications of Koopman Mode Analysis to Neural Networks
We also show how using Koopman modes we can selectively prune the network to speed up the training procedure.
Data-driven spectral analysis of the Koopman operator
We also show how to compute, from measured data, the spectral projection on a given segment of the unit circle, allowing us to obtain a finite-dimensional approximation of the operator that explicitly takes into account the point and continuous parts of the spectrum.
Dynamical Systems Numerical Analysis Spectral Theory
Study of dynamics in post-transient flows using Koopman mode decomposition
We observe that KMD outperforms the Proper Orthogonal Decomposition in reconstruction of the flows with strong quasi-periodic components. c features are present in the flow.
Fluid Dynamics 37N10
Ergodic theory, Dynamic Mode Decomposition and Computation of Spectral Properties of the Koopman operator
We establish the convergence of a class of numerical algorithms, known as Dynamic Mode Decomposition (DMD), for computation of the eigenvalues and eigenfunctions of the infinite-dimensional Koopman operator.
Dynamical Systems 37M10, 37A30, 65P99, 37N10
