A two-layer PINN can be trained by SGD to O(epsilon) loss with width independent of the number of samples, provided the target lies in a custom function class and the SGD trajectory does not explode.
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Optimization and generalization analysis for two-layer physics-informed neural networks without over-parametrization
A two-layer PINN can be trained by SGD to O(epsilon) loss with width independent of the number of samples, provided the target lies in a custom function class and the SGD trajectory does not explode.