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The paper proposes a class of time-varying deep state-space models where dynamics are learned via a basis function expansion, enabling adaptive modeling of switching systems. The approach outperforms time-invariant counterparts on synthetic switching data and a speech denoising task.
This paper extends Port-Hamiltonian Neural Networks (PHNNs) to partial differential equations (PDEs) for learning nonlinear string dynamics from data. The approach recovers both the Hamiltonian and dissipation, outperforming non-physics-informed baselines in accuracy and interpretability.