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

• URL: http://arxiv.org/abs/2607.18164v1
• Date: Mon, 20 Jul 2026 17:07:41 GMT
• Title: A Continual Validation, Updating, and Decision-Making Framework for Self-Adaptive Digital Twins via Robust Model Predictive Control: A Case Study in Additive Manufacturing
• Abstract summary: Digital Twins rely on surrogate models to mirror physical systems in real time, yet these models can degrade as operating conditions evolve.<n>Existing adaptive frameworks lack principled mechanisms for detecting when updates are needed.<n>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.
• Score: 11.700555168494917
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Navigating the Latent Manifold: Proactive Concept Drift Adaptation for Resilient NIDS [1.9678915302433768]
  We propose DriftXpert, a novel NIDS for drift-adaptive detection.<n>In Phase 1, we introduce an unsupervised anomaly metric based on latent manifold deviation.<n>In Phase 2, to mitigate catastrophic forgetting under non-stationary distributions, we design a representation consistency alignment strategy.
  arXiv  Detail & Related papers  (2026-09-08T11:58:12Z)
• Joint Model and Data Sparsification via the Marginal Likelihood [53.29070892356214]
  We propose jointly learning individual feature and sample relevancies, enabling simultaneous model and data sparsification.<n>This symmetric pruning of model and data offers a natural extension that preserves conjugacy.<n> Empirical results across diverse regression tasks affirm that a joint ARD approach consistently yields both sparse and robust prediction models.
  arXiv  Detail & Related papers  (2026-05-28T13:26:53Z)
• ResAD: Normalized Residual Trajectory Modeling for End-to-End Autonomous Driving [64.42138266293202]
  ResAD is a Normalized Residual Trajectory Modeling framework.<n>It reframes the learning task to predict the residual deviation from an inertial reference.<n>On the NAVSIM benchmark, ResAD achieves a state-of-the-art PDMS of 88.6 using a vanilla diffusion policy.
  arXiv  Detail & Related papers  (2025-10-09T17:59:36Z)
• Revisiting Multivariate Time Series Forecasting with Missing Values [65.30332997607141]
  Missing values are common in real-world time series.<n>Current approaches have developed an imputation-then-prediction framework that uses imputation modules to fill in missing values, followed by forecasting on the imputed data.<n>This framework overlooks a critical issue: there is no ground truth for the missing values, making the imputation process susceptible to errors that can degrade prediction accuracy.<n>We introduce Consistency-Regularized Information Bottleneck (CRIB), a novel framework built on the Information Bottleneck principle.
  arXiv  Detail & Related papers  (2025-09-27T20:57:48Z)
• Dynamic Aware: Adaptive Multi-Mode Out-of-Distribution Detection for Trajectory Prediction in Autonomous Vehicles [8.920589816043298]
  Trajectory prediction is central to the safe and seamless operation of autonomous vehicles.<n>In deployment, prediction models inevitably face distribution shifts between training data and real-world conditions.<n>We propose a new framework that introduces adaptive mechanisms to achieve robust detection in complex driving environments.
  arXiv  Detail & Related papers  (2025-09-16T22:37:21Z)
• Improving Real-Time Concept Drift Detection using a Hybrid Transformer-Autoencoder Framework [0.0]
  In applied machine learning, concept drift can significantly reduce model performance.<n>Our study proposes a hybrid framework consisting of Transformers and Autoencoders to model complex temporal dynamics.<n>Our results support that the Transformation-Autoencoder detected drift earlier and with more sensitivity than the autoencoders commonly used in the literature.
  arXiv  Detail & Related papers  (2025-08-09T19:39:33Z)
• Detecting Concept Drift in Neural Networks Using Chi-squared Goodness of Fit Testing [0.0]
  Concept drift detection is a field dedicated to identifying statistical shifts that is underutilized in monitoring neural networks.<n>We introduce an application of the $chi2$ Goodness of Fit Hypothesis Test as a drift detection meta-algorithm applied to a multilayer perceptron, a convolutional neural network, and a transformer trained for machine vision.
  arXiv  Detail & Related papers  (2025-05-07T11:04:47Z)
• AutoML for Multi-Class Anomaly Compensation of Sensor Drift [44.63945828405864]
  Sensor drift degrades the performance of machine learning models over time.<n>Standard cross-validation method overestimates performance by inadequately accounting for drift.<n>This paper presents two solutions: (1) a novel sensor drift compensation learning paradigm for validating models, and (2) automated machine learning (AutoML) techniques to enhance classification performance and compensate sensor drift.
  arXiv  Detail & Related papers  (2025-02-26T14:34:53Z)
• EdgeFD: An Edge-Friendly Drift-Aware Fault Diagnosis System for Industrial IoT [0.0]
  We propose the Drift-Aware Weight Consolidation (DAWC) to mitigate the challenges posed by frequent data drift in the industrial Internet of Things (IIoT) DAWC efficiently manages multiple data drift scenarios, minimizing the need for constant model fine-tuning on edge devices. We have also developed a comprehensive diagnosis and visualization platform.
  arXiv  Detail & Related papers  (2023-10-07T06:48:07Z)
• End-to-End Reinforcement Learning of Koopman Models for Economic Nonlinear Model Predictive Control [45.84205238554709]
  We present a method for reinforcement learning of Koopman surrogate models for optimal performance as part of (e)NMPC. We show that the end-to-end trained models outperform those trained using system identification in (e)NMPC.
  arXiv  Detail & Related papers  (2023-08-03T10:21:53Z)
• Confidence Attention and Generalization Enhanced Distillation for Continuous Video Domain Adaptation [62.458968086881555]
  Continuous Video Domain Adaptation (CVDA) is a scenario where a source model is required to adapt to a series of individually available changing target domains. We propose a Confidence-Attentive network with geneRalization enhanced self-knowledge disTillation (CART) to address the challenge in CVDA.
  arXiv  Detail & Related papers  (2023-03-18T16:40:10Z)
• Anomaly Detection of Time Series with Smoothness-Inducing Sequential Variational Auto-Encoder [59.69303945834122]
  We present a Smoothness-Inducing Sequential Variational Auto-Encoder (SISVAE) model for robust estimation and anomaly detection of time series. Our model parameterizes mean and variance for each time-stamp with flexible neural networks. We show the effectiveness of our model on both synthetic datasets and public real-world benchmarks.
  arXiv  Detail & Related papers  (2021-02-02T06:15:15Z)
• Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift [100.52588638477862]
  We develop an approximate Bayesian inference scheme based on posterior regularisation. We demonstrate the utility of our method in the context of transferring prognostic models of prostate cancer across globally diverse populations.
  arXiv  Detail & Related papers  (2020-06-26T13:50:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.