Pages that link to "Item:Q2115518"
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The following pages link to Data-driven approximation of the Koopman generator: model reduction, system identification, and control (Q2115518):
Displaying 13 items.
- Learning dynamical systems from data: a simple cross-validation perspective. V: Sparse kernel flows for 132 chaotic dynamical systems (Q6496480) (← links)
- A data-driven framework for learning hybrid dynamical systems (Q6548679) (← links)
- The evolving butterfly: statistics in a changing attractor (Q6554901) (← links)
- Simplicity bias, algorithmic probability, and the random logistic map (Q6554925) (← links)
- Propagating uncertainty through system dynamics in reproducing kernel Hilbert space (Q6554937) (← links)
- Bridging algorithmic information theory and machine learning: a new approach to kernel learning (Q6558847) (← links)
- Hausdorff metric based training of kernels to learn attractors with application to 133 chaotic dynamical systems (Q6558876) (← links)
- Learning the temporal evolution of multivariate densities via normalizing flows (Q6560595) (← links)
- An end-to-end deep learning approach for extracting stochastic dynamical systems with \(\alpha\)-stable Lévy noise (Q6565156) (← links)
- Learning effective stochastic differential equations from microscopic simulations: linking stochastic numerics to deep learning (Q6572673) (← links)
- Existence and uniqueness of solutions of the Koopman-von Neumann equation on bounded domains (Q6612272) (← links)
- EDMD for expanding circle maps and their complex perturbations (Q6657422) (← links)
- Chaotic fields out of equilibrium are observable independent (Q6669484) (← links)
