Operator-theoretic framework for forecasting nonlinear time series with kernel analog techniques

2019/06/30 by Romeo Alexander, Dimitrios Giannakis · 8 citations
Decision Sciences · Engineering · Physics and Astronomy · #Fluid Dynamics and Turbulent Flows #Model Reduction and Neural Networks #Probabilistic and Robust Engineering Design

paper · doi:10.1016/j.physd.2020.132520

openalex publication_date 2020/04/14 · openalex created_date 2020/04/24 · openalex updated_date 2026/07/30

Citations

• A Data-Driven Approximation of the Koopman Operator: Extending Dynamic Mode Decomposition
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• Comparison of systems with complex behavior
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• On Learning Vector-Valued Functions
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• Atmospheric Predictability as Revealed by Naturally Occurring Analogues
• Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert Spaces
• Koopman spectra in reproducing kernel Hilbert spaces
• Universality, Characteristic Kernels and RKHS Embedding of Measures
• Applied Koopmanism
• Diffusion maps
• Embedology

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• Data-efficient Kernel Methods for Learning Hamiltonian Systems
• Retrodicting Chaotic Systems: An Algorithmic Information Theory Approach
• Extracting transient Koopman modes from short-term weather simulations with sparsity-promoting dynamic mode decomposition
• Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems
• Learning dynamical systems from data: A simple cross-validation perspective, part I: Parametric kernel flows
• Learning Dynamical Systems with the Spectral Exterior Calculus