Kernel methods for detecting coherent structures in dynamical data
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Publication:5213519
DOI10.1063/1.5100267zbMath1448.37100arXiv1904.07752OpenAlexW3103060822WikidataQ92362351 ScholiaQ92362351MaRDI QIDQ5213519
Mattes Mollenhauer, Frank Noé, Brooke E. Husic, Stefan Klus
Publication date: 3 February 2020
Published in: Chaos: An Interdisciplinary Journal of Nonlinear Science (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1904.07752
Related Items (6)
Koopman analysis of quantum systems* ⋮ Overcoming the timescale barrier in molecular dynamics: Transfer operators, variational principles and machine learning ⋮ Singular Value Decomposition of Operators on Reproducing Kernel Hilbert Spaces ⋮ The linear conditional expectation in Hilbert space ⋮ Tensor-based computation of metastable and coherent sets ⋮ Koopman-based spectral clustering of directed and time-evolving graphs
Uses Software
Cites Work
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- A kernel-based method for data-driven Koopman spectral analysis
- On the numerical approximation of the Perron-Frobenius and Koopman operator
- Fuzzy spectral clustering by PCCA+: application to Markov state models and data classification
- Data-driven model reduction and transfer operator approximation
- A data-driven approximation of the koopman operator: extending dynamic mode decomposition
- A direct approach to conformational dynamics based on hybrid Monte Carlo
- Chaos, fractals, and noise: Stochastic aspects of dynamics.
- Rates of convergence for everywhere-positive Markov chains
- Transition manifolds of complex metastable systems. Theory and data-driven computation of effective dynamics
- An analytic framework for identifying finite-time coherent sets in time-dependent dynamical systems
- On dynamic mode decomposition: theory and applications
- 10.1162/153244303768966085
- Dynamic mode decomposition of numerical and experimental data
- Support Vector Machines
- Joint Measures and Cross-Covariance Operators
- Identifying finite-time coherent sets from limited quantities of Lagrangian data
- On fast computation of finite-time coherent sets using radial basis functions
- Kernel Mean Embedding of Distributions: A Review and Beyond
- Understanding the geometry of transport: Diffusion maps for Lagrangian trajectory data unravel coherent sets
- Extended dynamic mode decomposition with dictionary learning: A data-driven adaptive spectral decomposition of the Koopman operator
- Robust FEM-Based Extraction of Finite-Time Coherent Sets Using Scattered, Sparse, and Incomplete Trajectories
- Kernel-Based Nonlinear Blind Source Separation
- Randomized Dynamic Mode Decomposition
- Transport in time-dependent dynamical systems: Finite-time coherent sets
- Linearly Recurrent Autoencoder Networks for Learning Dynamics
- A Variational Approach to Modeling Slow Processes in Stochastic Dynamical Systems
- Mutual Information for Gaussian Processes
- RELATIONS BETWEEN TWO SETS OF VARIATES
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