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研究表明LLMs提高了工程模拟器的校准效率Study shows LLMs enhance engineering simulator calibration efficiency
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以下内容为 AI 自动翻译，英文原文附后供参考。
摘要
最近的一项研究探讨了大型语言模型（LLMs）在工程模拟器校准中的应用。通过利用物理标记提示，研究表明LLMs能够有效地根据物理诊断描述提出参数更新。研究结果表明，当将LLMs与传统优化技术结合时，校准效率显著提高，暗示了增强工程模拟的有希望的途径。
A recent study investigates the application of Large Language Models (LLMs) in the calibration of engineering simulators. By utilizing physics-labeled prompting, the research demonstrates that LLMs can effectively propose parameter updates based on physical diagnostic descriptions. The findings indicate a notable improvement in calibration efficiency when LLMs are integrated with traditional optimization techniques, suggesting a promising avenue for enhancing engineering simulations.
要点
LLMs可以显著提高工程模拟器的校准效率。
LLMs can significantly improve the calibration efficiency of engineering simulators.
将LLMs与传统优化方法结合可以获得更好的结果。
Combining LLMs with conventional optimization methods yields better results.
物理标记提示是利用LLMs实现这一目的的关键技术。
Physics-labeled prompting is a key technique in utilizing LLMs for this purpose.
为何重要
这项研究突显了LLMs在简化工程流程方面的潜力，这可能导致更准确的模拟和更快的开发周期。
This research highlights the potential of LLMs to streamline engineering processes, which could lead to more accurate simulations and faster development cycles.
产品经理
产品经理应考虑LLMs在优化工程工作流程中的影响，可能减少新产品的上市时间。
Product managers should consider the implications of LLMs in optimizing engineering workflows, potentially reducing time-to-market for new products.
开发者
开发人员可以利用这项研究将LLMs集成到他们的校准过程中，提高工程模拟器的性能和可靠性。
Developers can leverage this research to integrate LLMs into their calibration processes, enhancing the performance and reliability of engineering simulators.
学生与求职者
工程和AI领域的学生及求职者可以通过了解LLMs如何转变传统实践而受益，从而在就业市场中更具竞争力。
Students and job-seekers in engineering and AI fields can benefit from understanding how LLMs are transforming traditional practices, making them more competitive in the job market.
覆盖来源
来源标题与摘录保留原文，以便核对与追溯。
arXiv cs.LG
When is a large language model a good calibration agent? Physics-labeled prompting for budgeted engineering simulator calibrationThis study explores the use of Large Language Models (LLMs) for calibrating engineering simulators by proposing parameter updates based on physical diagnostic descriptions. The results show that LLMs can significantly improve calibration efficiency, particularly when combined with conventional optimization methods.