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Anomaly detection in dynamical systems from measured time series
The paper addresses a problem of abnormalities detection in nonlinear processes represented by measured time series.
Physics-based polynomial neural networks for one-shot learning of dynamical systems from one or a few samples
This paper discusses an approach for incorporating prior physical knowledge into the neural network to improve data efficiency and the generalization of predictive models.
Polynomial Neural Networks and Taylor maps for Dynamical Systems Simulation and Learning
The connection of Taylor maps and polynomial neural networks (PNN) to solve ordinary differential equations (ODEs) numerically is considered.
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