Solving the inverse source problem of the fractional Poisson equation by MC-fPINNs

In this paper, we effectively solve the inverse source problem of the fractional Poisson equation using MC-fPINNs. We construct two neural networks $u_{NN}(x;\theta )$ and $f_{NN}(x;\psi )$ to approximate the solution $u^{\ast }(x)$ and the forcing term $f^{\ast }(x)$ of the fractional Poisson equation. To optimize these two neural networks, we use the Monte Carlo sampling method mentioned in MC-fPINNs and define a new loss function combining measurement data and the underlying physical model. Meanwhile, we present a comprehensive error analysis for this method, along with a prior rule to select the appropriate parameters of neural networks. Several numerical examples are given to demonstrate the great precision and robustness of this method in solving high-dimensional problems up to 10D, with various fractional order $\alpha$ and different noise levels of the measurement data ranging from 1$\mathrm{\%}$ to 10$\mathrm{\%}$.