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Found 9 papers, 8 papers with code
A randomized algorithm to solve reduced rank operator regression
We present and analyze an algorithm designed for addressing vector-valued regression problems involving possibly infinite-dimensional input and output spaces.
Consistent Long-Term Forecasting of Ergodic Dynamical Systems
We study the evolution of distributions under the action of an ergodic dynamical system, which may be stochastic in nature.
Dynamics Harmonic Analysis of Robotic Systems: Application in Data-Driven Koopman Modelling
We introduce the use of harmonic analysis to decompose the state space of symmetric robotic systems into orthogonal isotypic subspaces.
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.
Transfer learning for atomistic simulations using GNNs and kernel mean embeddings
Interatomic potentials learned using machine learning methods have been successfully applied to atomistic simulations.
Learning dynamical systems: an example from open quantum system dynamics
Machine learning algorithms designed to learn dynamical systems from data can be used to forecast, control and interpret the observed dynamics.
Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces
We formalize a framework to learn the Koopman operator from finite data trajectories of the dynamical system.
Characterizing metastable states with the help of machine learning
Present-day atomistic simulations generate long trajectories of ever more complex systems.
