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Found 3 papers, 1 papers with code
Koopman-Assisted Reinforcement Learning
In particular, the Koopman operator is able to capture the expectation of the time evolution of the value function of a given system via linear dynamics in the lifted coordinates.
On the Relationships between Graph Neural Networks for the Simulation of Physical Systems and Classical Numerical Methods
Recent developments in Machine Learning approaches for modelling physical systems have begun to mirror the past development of numerical methods in the computational sciences.
Sparse Identification of Truncation Errors
We augment a sparse regression-rooted approach with appropriate preconditioning routines to aid in the identification of the individual modified differential equation terms.
Numerical Analysis 62J05, 65F08 (Primary) 90C31, 35Q35, 68W40 (Secondary) G.1.8; G.3; F.2.0
