<ul><li>Researchers propose a novel continuous-time domain hybrid modeling paradigm for nonlinear dynamics system identification.</li><li>The hybrid model integrates neural network differential models with recurrent neural networks (RNNs).</li><li>Theoretical analysis demonstrates advantages in event-driven dynamic mutation response and gradient propagation stability.</li><li>Validation using real data shows improved fitting accuracy and effectiveness in capturing nonlinear memory effects.</li></ul>

Hybrid Time-Domain Behavior Model Based on Neural Differential Equations and RNNs

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