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

arXiv: https://arxiv.org/abs/2607.18164 · HTML: https://mlsanity.com/paper/2607.18164/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 99 references that are in the map.
- First posted on arXiv 20 Jul 2026.

## Builds on (most cited first, 26 of 26)

- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Optimizing Neural Networks with Kronecker-factored Approximate Curvature](https://mlsanity.com/paper/1503.05671/) (2015)
- [Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning](https://mlsanity.com/paper/2012.13255/) (2020)
- [Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey](https://mlsanity.com/paper/2403.14608/) (2024)
- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Long-term Forecasting with TiDE: Time-series Dense Encoder](https://mlsanity.com/paper/2304.08424/) (2023)
- [Cautious Model Predictive Control using Gaussian Process Regression](https://mlsanity.com/paper/1705.10702/) (2017)
- [A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data Streams](https://mlsanity.com/paper/2104.10529/) (2021)
- [A Comprehensive Review of Digital Twin -- Part 1: Modeling and Twinning Enabling Technologies](https://mlsanity.com/paper/2208.14197/) (2022)
- [Catastrophic Forgetting in Deep Learning: A Comprehensive Taxonomy](https://mlsanity.com/paper/2312.10549/) (2023)
- [Online Dynamics Learning for Predictive Control with an Application to Aerial Robots](https://mlsanity.com/paper/2207.09344/) (2022)
- [Deep Learning Tubes for Tube MPC](https://mlsanity.com/paper/2002.01587/) (2020)
- [Concept Drift Monitoring and Diagnostics of Supervised Learning Models via Score Vectors](https://mlsanity.com/paper/2012.06916/) (2020)
- [Towards a Digital Twin Framework in Additive Manufacturing: Machine Learning and Bayesian Optimization for Time Series Process Optimization](https://mlsanity.com/paper/2402.17718/) (2024)
- [Real-Time Decision-Making for Digital Twin in Additive Manufacturing with Model Predictive Control using Time-Series Deep Neural Networks](https://mlsanity.com/paper/2501.07601/) (2025)
- [State space models vs. multi-step predictors in predictive control: Are state space models complicating safe data-driven designs?](https://mlsanity.com/paper/2203.15471/) (2022)
- [Reinforcement Twinning: from digital twins to model-based reinforcement learning](https://mlsanity.com/paper/2311.03628/) (2023)
- [Targeted Digital Twin via Flow Map Learning and Its Application to Fluid Dynamics](https://mlsanity.com/paper/2510.07549/) (2025)
- [Uncertainty-Aware Digital Twins: Robust Model Predictive Control using Time-Series Deep Quantile Learning](https://mlsanity.com/paper/2501.10337/) (2025)
- [Verification and Validation for Trustworthy Scientific Machine Learning](https://mlsanity.com/paper/2502.15496/) (2025)
- [Online Weight-adaptive Nonlinear Model Predictive Control](https://mlsanity.com/paper/2008.02532/) (2020)
- [Unmatched uncertainty mitigation through neural network supported model predictive control](https://mlsanity.com/paper/2304.11315/) (2023)
- [Data-driven Acceleration of MPC with Guarantees](https://mlsanity.com/paper/2511.13588/) (2025)
- [2026 Roadmap on Artificial Intelligence and Machine Learning for Smart Manufacturing](https://mlsanity.com/paper/2605.00839/) (2026)
- [Adaptive Digital Twin of Sheet Metal Forming via Proper Orthogonal Decomposition-Based Koopman Operator with Model Predictive Control](https://mlsanity.com/paper/2511.10852/) (2025)
- [When an Approximate Model Suffices for Optimal Control](https://mlsanity.com/paper/2601.09826/) (2026)

## Abstract (arXiv)

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.