Abstract
Decomposes dynamical system dynamics into conservation, dissipation, and external force components using separate sub-networks with port-Hamiltonian structure. The approach outperforms standard neural networks on dynamical systems benchmarks and produces models that remain valid when external forces are modified. Based on the phlearn package from SINTEF.