Projected gradient descent for one-hidden-layer PINNs solving the Poisson equation is claimed to converge with average loss O(1/sqrt(T) + 1/sqrt(m)) plus an approximation error, with a generalization bound via Rademacher complexity of the Laplacian.
Three ways to solve partial differential equations with neural networks—A review
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Non-Asymptotic Analysis of Projected Gradient Descent for Physics-Informed Neural Networks
Projected gradient descent for one-hidden-layer PINNs solving the Poisson equation is claimed to converge with average loss O(1/sqrt(T) + 1/sqrt(m)) plus an approximation error, with a generalization bound via Rademacher complexity of the Laplacian.