Engineering practitioners report that graph neural networks and MLPs on parameterized designs offer the best practical balance for predicting fields like temperature and stress. Data efficiency is achievable with 10–50 training samples, especially when transfer learning is applied across similar geometries. Physics-informed neural networks (PINNs) remain largely experimental for complex engineering geometries, with most users relying on data-driven surrogates. Generalization remains a key challenge, with models often failing on out-of-distribution boundary conditions, prompting a return to full solver runs.