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When is a large language model a good calibration agent? Physics-labeled prompting for budgeted engineering simulator calibration
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calibrationoptimizationlarge language modelsengineering
2610.22379
Builder Relevance
1h ago80%
Abstract
This study investigates the efficiency of Large Language Models (LLMs) as calibration agents for engineering simulators compared to conventional optimizers.
Reality Card
Core Claim
The hybrid workflow using LLMs as a warm-start significantly reduces the number of simulator runs needed to achieve target accuracy, outperforming conventional optimization methods.
Method / Result
The hybrid physics-labeled LLM scheme reaches target accuracy in just 9 simulator runs.
Limitations
The advantage of LLMs is transient and subject to inherent decoder variability, leading to a 9.8-fold performance spread.
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