Hamiltonian neural networks with automatic symmetry detection

What is it about?

Incorporating physical system knowledge into data-driven system identification has been shown to be beneficial. The approach presented in this article combines the learning of an energy conserving model from data with detecting a Lie group representation of the unknown system symmetry. The proposed approach can improve the learned model and reveal underlying symmetry simultaneously.

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