Abstract
AbstractPort‐Hamiltonian neural networks can be used to capture the interactions between small interconnected port‐Hamiltonian systems from data. We now examine different composed models and check how accurate we can train the modified composed systems. The learning of the dynamics of the composed systems is investigated for cases in which the interconnection is known beforehand and also for cases without knowledge about the interconnection. In the latter cases, only small additional data is required.
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