A Continual Validation, Updating, and Decision-Making Framework for Self-Adaptive Digital Twins via Robust Model Predictive Control: A Case Study in Additive Manufacturing

2026-07-20

AI Summary

This work addresses the vulnerability of surrogate models in digital twins to concept drift under shifting operational conditions, which degrades both predictive accuracy and uncertainty quantification. To mitigate this, the authors propose an adaptive digital twin framework that integrates multivariate distribution drift detection based on Fisher scores, a parameter-efficient LoRA-based continual learning mechanism for model adaptation, and Mann-Whitney U test–driven online statistical validation to assess the necessity of updates, enable efficient fine-tuning, and ensure reliability. Evaluated on a stochastic linear system and a directed energy deposition additive manufacturing task, the approach significantly accelerates drift detection, enhances model recovery accuracy, and improves the quality of uncertainty estimates, thereby enabling trustworthy continuous deployment of surrogate models.

Abstract

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.

Problem

• Digital Twins
• concept drift
• surrogate models
• aleatoric uncertainty
• continual learning

Innovation

• Digital Twin
• Concept Drift
• Low-Rank Adaptation (LoRA)
• Fisher Score
• Statistical Validation