这篇论文解决了数字孪生中模型漂移的老大难问题，用LoRA和统计检验做到实时更新，增材制造案例表现很好。
该论文提出一种自适应数字孪生框架，集成基于Fisher分数的多变量漂移检测器，可实时监测代理模型置信度。在检测到概念漂移时，仅对少于1%的模型参数进行LoRA微调，并采用Mann-Whitney U检验统计验证预测性能提升。在随机线性系统和定向能量沉积增材制造案例中，该框架能快速检测分布偏移并恢复预测精度与不确定性量化。结果表明该方法能为神经网络数字孪生提供统计严谨、计算可行的持续可信保障。
A Continual Validation, Updating, and Decision-Making Framework for Self-Adaptive Digital Twins via Robust Model Predictive Control: A Case Study in Additive Manufacturing
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.