Physics-based polynomial neural networks for one-shot learning of dynamical systems from one or a few samples

Understand

This paper discusses an approach for incorporating prior physical knowledge into the neural network to improve data efficiency and the generalization of predictive models.

• If the dynamics of a system approximately follows a given differential equation, the Taylor mapping method can be used to initialize the weights of a polynomial neural network.
• This allows the fine-tuning of the model from one training sample of real system dynamics.
• The paper describes practical results on real experiments with both a simple pendulum and one of the largest worldwide X-ray source.