A DeepONet-style network learns a Green's function and its boundary gradient, then solves 3D linear PDEs by numerical integration, outperforming four standard neural operator baselines.
Approximation theory of the mlp model in neural networks
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An explainable operator approximation framework under the guideline of Green's function
A DeepONet-style network learns a Green's function and its boundary gradient, then solves 3D linear PDEs by numerical integration, outperforming four standard neural operator baselines.