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Found 2 papers, 2 papers with code
Learning invariant representations of time-homogeneous stochastic dynamical systems
We consider the general class of time-homogeneous stochastic dynamical systems, both discrete and continuous, and study the problem of learning a representation of the state that faithfully captures its dynamics.
Estimating Koopman operators with sketching to provably learn large scale dynamical systems
Our empirical and theoretical analysis shows that the proposed estimators provide a sound and efficient way to learn large scale dynamical systems.
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