Fig. 7: The multi-level physics-informed neural network (ml-PINN) for generalization computation. | Communications Engineering

Fig. 7: The multi-level physics-informed neural network (ml-PINN) for generalization computation.

From: Multi-level physics informed deep learning for solving partial differential equations in computational structural mechanics

Fig. 7

a Framework of ml-PINN for generalization learning by incorporating the physical variables related to loading conditions (u and q) as input parameters of the networks. u and q represent the displacement applied on the right boundary and the uniform load imposed on the entire region. b Direct prediction results of ml-PINN for bending beam cases under different loading conditions. FEM represents the result predicted by Finite Element Method, and Generalized PINN represents the result predicted by the proposed ml-PINN.

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