AI-Assisted Model Generation

Artificial intelligence is already being used in many engineering and scientific workflows. But engineering professionals, researchers, and educators recognize that AI-generated output still requires careful review. It may misunderstand assumptions, omit important details, or present a plausible-looking formulation that does not fully match the intended physical problem.

From this point of view, workflows where AI assists with model preparation, while humans can review the generated formulation, inspect the model, and remain responsible for the final engineering decision, may offer a more reliable approach.

QuickField uses AI in this way. The AI assistant does not calculate the field and does not produce simulation results directly. It does not replace finite element analysis by prediction, interpolation, or extrapolation of previously calculated results.

Instead, AI helps prepare compact, human-readable JSON metafiles describing the QuickField problem. These metafiles are converted into native QuickField files by the free QuickField Builders. The final field solution is obtained by solving the governing field equations using the same finite element solver as in conventional QuickField workflows.

The JSON metafiles provide a reviewable intermediate layer between a natural-language task description and native QuickField files. This keeps the human in control from the initial task description to the generated QuickField problem and final simulation results.

Practical examples are collected on the AI Use Cases page.