AI-Assisted Model Generation

Artificial intelligence is already used in many engineering and research workflows. But engineers, researchers and teachers all know that AI-generated results still need careful review — it can misread assumptions, miss important details, or produce a statement that looks plausible but doesn't quite match the intended physical problem.

From this perspective, a workflow where AI helps prepare the model while a person reviews the generated statement, checks the model, and remains responsible for the final engineering decision may be more reliable.

This is exactly how QuickField uses AI. The AI assistant does not compute the field, and it does not produce simulation results directly; it does not replace finite element analysis by predicting, interpolating or extrapolating from existing computed results.

Instead, AI's role is to help prepare a compact, readable JSON metafile that describes a QuickField problem. These metafiles are converted into native QuickField files by the free QuickField Builders. The final field solution is still obtained by solving the field equations with the same finite element solver used in the regular QuickField workflow.

The JSON metafile provides a reviewable intermediate layer between the natural-language task description and the native QuickField file. This keeps the entire process — from the initial task description, to the generated QuickField problem, to the final simulation result — in human hands.