Can LLMs effectively calibrate engineering simulators under budget constraints?
In brief
- LLMs propose more efficient physical parameter updates than classical optimizers in early calibration stages.
- The LLM advantage vanishes after 15 evaluations, but using them as a warm-start reduces costs by 47% compared to standard methods.
- A hybrid approach (LLM + optimizer) reaches target accuracy in just 9 evaluations, versus over 60 for Bayesian optimization.
This study assesses the effectiveness of large language models in calibrating expensive engineering simulators by suggesting physical parameter updates. Results show a significant early advantage, but it fades over time, requiring a hybrid approach for sustained gains.
What it changes for you
- Industry & ManufacturingLLMs speed up engineering simulator calibration, cutting R&D costs and improving the accuracy of physical models.
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