Using the FEM formulation as a loss function in the neural network such that the solution learned by the network satisfies the governing equations, boundary conditions, and the variational form imposed by FEM. This approach leverages the physical interpretability and mesh-based structure of FEM while incorporating the flexibility and data-driven capabilities of neural networks.