AI-assisted wind engineering (responsibly)

AI can accelerate — but physics must lead

Generative AI is most valuable when it reduces friction in engineering delivery, not when it replaces governing physics.

High-value AI use cases in DynaWind workflows

• Setup guidance: checklists, assumptions, and parameter sanity checks.
• Result triage: spotting outliers, convergence issues, and likely sensitivity drivers.
• Reporting assistance: consistent narrative, figures, and traceability tables.
• Design iteration: comparing options and summarizing implications for stakeholders.

Where AI must be constrained

• AI should not “invent” boundary conditions, turbulence properties, or acceptance criteria.
• All assumptions must be explicitly documented and reviewable.
• Validation (wind tunnel / reference datasets) remains essential for high-risk decisions.