NeuroDiffEq implements physics-informed neural networks (PINNs) to solve differential equations without requiring labeled training data. The library encodes ODEs and PDEs as loss functions within the neural network training process, allowing the network to learn solutions that satisfy the governing equations. It provides tools for both initial value problems and boundary value problems.