A tensor train method computes the Koopman generator via operator logarithm while preserving low-rank structure for scalable identification of high-dimensional nonlinear dynamics.
Koopman operator, geometry, and learning of dy- namical systems
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A new data-synthesized instrumental variable estimator achieves finite-sample Lp consistency with sqrt(n) rate for linear-in-parameters models in discrete and continuous time, cutting bias by hundreds of times on Lorenz examples.
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Tensor-based computation of the Koopman generator via operator logarithm
A tensor train method computes the Koopman generator via operator logarithm while preserving low-rank structure for scalable identification of high-dimensional nonlinear dynamics.
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Instrumental variables system identification with $L^p$ consistency
A new data-synthesized instrumental variable estimator achieves finite-sample Lp consistency with sqrt(n) rate for linear-in-parameters models in discrete and continuous time, cutting bias by hundreds of times on Lorenz examples.