Neural port-Hamiltonian differential algebraic equations (N-PHDAEs) use known circuit topology with neural networks for unknown component behaviors and automatic differentiation for index reduction, showing better prediction and constraint satisfaction than a baseline neural ODE.
Neural Networks with Physics-Informed Architectures and Constraints for Dynamical Systems Modeling
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Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks
Neural port-Hamiltonian differential algebraic equations (N-PHDAEs) use known circuit topology with neural networks for unknown component behaviors and automatic differentiation for index reduction, showing better prediction and constraint satisfaction than a baseline neural ODE.