Physics-Informed Gated Neural Networks for Differential Equation Solvers


 Physics-Informed Gated Neural Networks for Differential Equation Solvers


This research introduces a physics-informed gated neural network (PIGNN) framework for solving ordinary and partial differential equations. By embedding governing physical laws directly into the loss function and incorporating gating mechanisms for adaptive feature control, the model improves convergence, generalization, and numerical stability.

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