We are developing a physics-informed neural network (PINN) to reconstruct neutron sources from extremely low detector counts. By embedding the detector response and imaging physics directly into the model, we expect the PINN will be able to recover source structure in regimes that traditional methods fail. This project will validate the approach using Geometry and Tracking v4 (GEANT4) nuclear physics simulations, creating a faster reconstruction than currently available methods.