发表机构
Northwestern University（西北大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对数字孪生中概念漂移致模型退化问题，提出集成Fisher分数漂移检测器、LoRA及Mann-Whitney U检验的自适应框架，能监测模型置信度、触发微调并统计验证性能提升，经案例研究成功检测变化并恢复相关性能，维持数字孪生可信度。
AI 中文摘要
数字孪生依赖替代模型实时反映物理系统，但随着运行条件变化，这些模型会退化，即概念漂移。在漂移情况下维持替代模型的保真度，尤其是在模型还需捕捉随机不确定性时，仍是一个开放挑战。现有自适应框架缺乏检测何时需要更新、从有限流数据高效适配模型以及证明更新确实能提高预测性能的原则性机制。本文提出一个自适应数字孪生框架，它集成了基于Fisher分数的多变量漂移检测器、用于参数高效持续学习的低秩自适应（LoRA）以及用于在线统计验证的Mann-Whitney U检验。该框架通过Fisher分数向量监测替代模型的置信度，在检测到漂移时触发对少于1%的模型参数进行有针对性的微调，并在部署更新后的替代模型之前通过统计证明预测性能的提升。通过应用于随机线性系统和定向能量沉积增材制造过程的案例研究，该框架成功地以短延迟检测到分布变化，并在突然和增量漂移下恢复了预测准确性和不确定性量化。这些结果为在神经网络数字孪生的整个生命周期中维持其可信度建立了一条统计严格且计算易处理的途径。
英文摘要
Digital Twins rely on surrogate models to mirror physical systems in real time, yet these models can degrade as operating conditions evolve, a phenomenon known as concept drift. Maintaining surrogate fidelity under drift, particularly when models must also capture aleatoric uncertainty, remains an open challenge. Existing adaptive frameworks lack principled mechanisms for detecting when updates are needed, for efficiently adapting models from limited streaming data, and for certifying that updates genuinely improve predictive performance. Here we present an adaptive Digital Twin framework that integrates a Fisher score--based multivariate drift detector, Low-Rank Adaptation (LoRA) for parameter-efficient continual learning, and a Mann--Whitney $U$ test for online statistical validation. The framework monitors surrogate-model confidence via Fisher score vectors, triggers targeted fine-tuning of fewer than 1% of model parameters upon drift detection, and statistically certifies predictive improvement before deploying the updated surrogate. Applied to a stochastic linear system and a directed energy deposition additive manufacturing process as case studies, the framework successfully detects distributional shifts with short delays and restores both predictive accuracy and uncertainty quantification under abrupt and incremental drift. These results establish a statistically rigorous and computationally tractable pathway for sustaining the trustworthiness of neural-network--based Digital Twins throughout their operational life cycle.
