Physics-Informed AI: Why LLMs Need Solvers, Constraints, and Physical Laws

Part 1 of 2 - An engineering view of how LLMs, physics-informed ML, and numerical solvers can work together without pretending that AI guarantees physical correctness. LLMs can produce fluent explanations of thermodynamics, fluid mechanics, and control theory. The concern, from an engineering perspective, is not whether an answer sounds correct. The concern is whether it satisfies conservation laws, boundary conditions, and operational constraints. Ask a general-purpose LLM to predict the pressure drop across a pipe given real boundary conditions and specific fluid parameters.