HIGHLIGHTS
- What: This study shows that by embedding an ODE-Net network layer in a physical information neural network (PINN) the fitting accuracy and generalization performance of the model can be significantly improved. This study has explored in detail the operation mechanism of Physical Information Neural_Networks (PINNs), and based on this, this paper proposes an innovative network architecture, ODENetPINN, which incorporates the features of Ordinary Differential Equation Networks (ODENet), especially the advantages in modeling continuous time series dynamical systems, to enhance the efficiency and accuracy of solving partial differential equations. This study explored the use of neural_networks, particularly . . .
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