AI-native engineering: let AI decide what deserves simulation first
Choose preselection with a hard validation gate
The choice is to use AI to rank early design variants, but not to turn that ranking into a release decision. Before each more intensive simulation, define which properties the option must demonstrate, who assesses the outcome, and when an option is eliminated. This is not a claim that AI can replace simulation. NIST describes this scope specifically for metal additive manufacturing: multi-physics and data-driven models are needed to simulate, study, and optimize processes, and they must first be validated before they can be used for process design or qualification of parts for medical and aerospace applications. For other engineering domains, that passage does not establish a general validation rule.
Treat simulation fidelity as an explicit design choice
Not every candidate needs the same model fidelity immediately. The Stanford passage mentions complex simulations for hardware optimization and agent training, and states that AI can help determine the simulation fidelity required and can create cheaper models that approximate high-fidelity simulations. Translate that into a working agreement: AI may propose an evidence-based order, while the team determines which simulation or test is still required at each stage. The passage does not substantiate accuracy, cost savings, or safety for an individual workflow; teams must demonstrate those through their own validation.
Document the transition from proposal to evidence
Use one decision log for each design variant: record the design variant, inputs used, AI prediction, selected simulation fidelity, owner, validation outcome, and follow-up decision. This keeps visible why an option may proceed to a more intensive stage and which assumptions underpin it. The practical application remains limited to the described source contexts and the quality of the available simulation and test data. This article does not determine the performance, certification, or suitability of a specific product; those questions require domain-specific validation and responsible assessment.
