How does loss functions work in PINN? [D]

Physics-informed neural networks (PINNs) use specialized loss functions that combine data-fitting terms with physics constraints derived from differential equations, allowing the network to learn solutions that satisfy both observed data and physical laws simultaneously. The loss function typically weights residuals from the governing equations alongside traditional supervised learning losses, enabling PINNs to solve inverse problems and discover differential equations from data with limited labeled examples. This approach leverages domain knowledge to regularize the learning process and improve generalization to physical systems.

Source: r/MachineLearning | 2026-05-19