用大模型让数字孪生实时自更新，无需重训就能适应新变量。
Continuously Updating Digital Twins using Large Language Models
- 基于大模型的上下文学习机制，实现推理时动态更新。
- 在多场景下保持高精度模拟，无需参数调整或重新训练。
- 适合需要持续迭代的复杂系统建模，如智能制造、智慧城市。
数字孪生是能够模拟现实系统对潜在动作响应的模型。在复杂环境中，系统状态、动作变量及可用数据和知识会持续变化，要求数字孪生能持续更新以保持相关性。现有方法受限于固定建模环境，无法在不重新设计的情况下适应新变量，也无法在不重新训练的前提下融入新信息。为此，本文将数字孪生建模视为一个上下文学习问题，利用大语言模型实现推理时的无缝更新。我们提出了CALM-DT——一种基于上下文自适应语言模型的数字孪生框架，仅通过上下文学习即可在多样化的状态-动作空间中准确模拟，借助微调后的编码器实现样本检索。实验表明，CALM-DT在性能上可与现有数字孪生方法媲美，并具备在不进行参数更新的情况下适应建模环境变化的独特能力。
原文摘要 · Abstract (English)
Digital twins are models of real-world systems that can simulate their dynamics in response to potential actions. In complex settings, the state and action variables, and available data and knowledge relevant to a system can constantly change, requiring digital twins to continuously update with these changes to remain relevant. Current approaches struggle in this regard, as they require fixed, well-defined modelling environments, and they cannot adapt to novel variables without re-designs, or incorporate new information without re-training. To address this, we frame digital twinning as an in-context learning problem using large language models, enabling seamless updates to the twin at inference time. We develop CALM-DT, a Context-Adaptive Language Model-based Digital Twin that can accurately simulate across diverse state-action spaces using in-context learning alone by utilising fine-tuned encoders for sample retrieval. We empirically demonstrate CALM-DT's competitive performance with existing digital twin approaches, and its unique ability to adapt to changes in its modelling environment without parameter updates.
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