  We propose, Monte Carlo Nonlocal physics-informed neural networks
(MC-Nonlocal-PINNs), which is a generalization of MC-fPINNs in
\cite{guo2022monte}, for solving general nonlocal models such as integral
equations and nonlocal PDEs. Similar as in MC-fPINNs, our MC-Nonlocal-PINNs
handle the nonlocal operators in a Monte Carlo way, resulting in a very stable
approach for high dimensional problems. We present a variety of test problems,
including high dimensional Volterra type integral equations, hypersingular
integral equations and nonlocal PDEs, to demonstrate the effectiveness of our
approach.
