Digital Transformation in Industry

Últimos artículos

• Grounding Time-Series Foundation Models in Digital Twin Topology for Predictive Maintenance

  Sizhe Ma, Katherine A. Flanigan, Mario Berg\'es · 1 de octubre de 2026

  Digital twins increasingly support downstream analytical tasks that depend on time-series data, motivating interest in time-series foundation models (TSFMs) as scalable backbones. However, TSFMs are primarily pretrained for temporal continuation and often underperform on unseen tasks such as regress…

• An Empirical Study of Architectural Shift from Traditional to AI-Enabled Simulink Controllers

  Hadiza Umar Yusuf, Khouloud Gaaloul · 1 de octubre de 2026

  Effective AI adoption in cyber-physical systems (CPS) depends on embedding design knowledge into engineering practice. Yet as AI-enabled components increasingly replace analytically derived control laws, this occurs without a systematic understanding of how controller architectures differ or remain …

• Digital Twins for Small Towns and Rural Regions: Data Integration, Simulation and Visualisation Across Three Use Cases in Lower Austria

  Thomas Delissen, Lukas Daniel Klausner, Maximilian Lackner, Torsten Priebe, L\H{o}rinc Thurnay · 29 de septiembre de 2026

  Smart city and digital twin concepts hold considerable potential for improving local governance and planning, yet practical implementations have almost exclusively focused on large metropolitan areas. Small towns and rural regions face a distinct set of challenges -- limited data availability, const…

• Autonomous Model Lifecycle Management for Digital Twin-Based Manufacturing Control

  Zhengyang (Cissy), Gu, Thomas Cook, Fredaljohn Rohrbaugh, Joseph E. Hernandez, Chris Couch · 22 de septiembre de 2026

  Manufacturing AI systems must autonomously adapt to continuous distributional shift from raw-material variability, ambient changes, and equipment aging, under strict safeguard and operator-trust requirements where model failures risk physical damage. This paper presents a closed-loop Cyber-Physical …

• GenOR-Twin: A Semantic Middleware for Integrating Operational Discourse with Mathematical Optimization

  Rahimeh Neamatian Monemi, Shahin Gelareh, Lubin Cui, Nelson Maculan · 14 de septiembre de 2026

  We introduce GenOR-Twin, a neuro-symbolic framework that bridges the translation gap between unstructured operational logs and rigorous mathematical optimization. Our architecture uniquely positions Large Language Models as semantic translators rather than direct solvers, ensuring that the system re…

• Involving before Evolving: A Vision for Trustworthy Enterprise Digital Twin Engineering

  K\'erian Fiter, Adil Lagrou, Franck Dervault, Bentley Oakes · 14 de septiembre de 2026

  Enterprise Digital Twins (EDTs) promise data-driven decision support at organizational scale, but realizing them requires navigating siloed departments, tacit knowledge, and high-stakes decisions with long-horizon consequences. Existing approaches involve domain experts during model development but …

• From State Synchronization to Cognitive Self-Evolution: An Operational Architecture for Cognitive Digital Twins

  Haoran Gao, An Li, Zhen Li, Jun Cai · 10 de septiembre de 2026

  As Digital Twin (DT) systems evolve beyond state synchronization toward task-oriented and knowledge-driven operation, Cognitive Digital Twins (CDTs) have emerged as an extension that incorporates cognitive capabilities into twin operation. Existing CDT studies often focus on specific enabling techni…

• When Can LLM Digital Twins Reduce Human Measurement? From Behavioral Fidelity to Statistical Substitutability

  Steven Wang, Kyle Hunt, Shaojie Tang, Kenneth Joseph · 9 de septiembre de 2026

  LLM-based digital twins promise to reduce repeated human data collection by generating person- specific responses, yet existing evaluations provide little evidence about whether they can reduce human measurement while preserving valid inference. To address this, we introduce statistical substitutabi…

• From Sensor Data to Classroom Inquiry: GenAI-Supported Exploration of School Digital Twin Data

  Themistoklis Sarantakos, Dimitrios Amaxilatis, Michail Giannakos, Georgios Mylonas · 9 de septiembre de 2026

  Digital Twins for educational buildings can support sustainability-oriented learning, but their use in schools remains limited. This paper presents a GenAI-based chatbot built on top of an existing Digital Twin for two school buildings in Greece, using real IoT data from environmental sensors and en…

• DNative-Twin: Decision Graphs and Digital Twins for Reconstructable Agentic Decisions

  Junjie Pang, Zhenzhen Xie, Haoke Han, Ying He, Jing Wang, Gang Liu · 4 de septiembre de 2026

  AI agents increasingly gather evidence, invoke tools, apply constraints, and produce decisions that people or software may commit to action. A final output alone cannot show which evidence, tool state, rule, authorization, or action path produced it. We present DNative-Twin, a graph-native digital t…

• ExploraTwin, a Non-Profit Research Platform for Digital Twin Simulations

  Naveen Venkat, Yuchen Qiu, Tianyi Peng, George Gui, Olivier Toubia · 26 de agosto de 2026

  Digital twin simulations show promise, but current empirical evidence suggests that the approach should be tested before being deployed in any particular context. To lower the friction for researchers and practitioners to test and deploy digital twin simulations, this brief commentary introduces Exp…

• Operational digital twin clinics enable task-based evaluation of embodied AI

  Xinyuan Wu, Jingrao Zhang, Mengdi Xu, Henry K. Chu, Mingguang He, Danli Shi · 25 de agosto de 2026

  Embodied artificial intelligence (AI) must be tested in the clinical environments where it will operate, but building realistic, robot-testable settings is costly and difficult to scale. Here we show that routine clinic images can be transformed into operational digital twins for task-based evaluati…

• Building real-time digital twin instances with Function+Data Flow: user evaluation and extension for iterative pipelines

  Eduardo de Conto, Blaise Genest, Arvind Easwaran, Nicholas Ng, Shweta Menon · 20 de agosto de 2026

  Digital twins (DTs) increasingly leverage artificial intelligence (AI) and machine learning (ML) pipelines, both to build real-time DTs from high-fidelity simulations and to instantiate them with historical data. However, engineering these pipelines remains largely ad-hoc: pipelines are hard to spec…

• AgenticTwin: An Agentic LLM Framework Integrated with Digital Twin for Anomaly Detection

  Touseef Hasan, Mounika Ghanta, Souvika Sarkar, Ujjwal Guin · 13 de agosto de 2026

  Digital twins are increasingly used to monitor and simulate the behavior of cyber-physical systems. Even with skilled operators, interpreting anomalies detected within digital twin pipelines is challenging, as the sheer complexity and volume of raw sensor data make thorough analysis difficult. Recen…

• A Conceptual Framework for Refining Influence Knowledge from Simulation Evidence in Cyber-Physical Systems

  Barbara da Silva Oliveira (UniCA, Laboratoire I3S - COMRED, KAIROS), Julien Deantoni (UniCA, Laboratoire I3S - COMRED, KAIROS), Nicolas Ferry (Laboratoire I3S - COMRED, KAIROS, UniCA) · 13 de agosto de 2026

  Cyber-physical systems (CPS) are typically developed by multiple stakeholders who produce artefacts tailored to their specific domains of expertise. The behaviour of these systems emerges from the interaction between those artefacts and their operational environment. Simulation and co-simulation hav…

• A Conceptual Framework for Enhancing Workforce Readiness for Smart Manufacturing in the AI Era

  Dalton Ross Smith, Wilburn Whittington, Alejandro Martinez, Aidan Duncan, Gang Li · 13 de agosto de 2026

  The convergence of artificial intelligence (AI), Industrial Internet of Things, cyber-physical systems, and advanced robotics is reshaping manufacturing faster than engineering curricula can adapt, widening the gap between the competencies required on the shop floor and those delivered by traditiona…

• From Passive Mirrors to Active Agents: Holonic Digital Twins for Physical AI over Networks

  Christo Kurisummoottil Thomas, Omar Hashash, Walid Saad · 7 de agosto de 2026

  Despite advances in artificial intelligence (AI) across multiple sectors, today's AI tools, including deep learning and generative AI, still fail when embedded into physical systems, such as robots and vehicles operating under real-world physical laws. This stems from their inability to maintain rel…

• Cardiovascular Digital Twins from Physics Based to Data Driven Approaches

  Emmanuel Lwele, Francis Chikweto · 4 de agosto de 2026

  Cardiovascular digital twins aim to create patient-specific computational models that evolve with clinical data to support diagnosis, prognosis, and therapy optimisation. Mechanistic models provide physiological interpretability but remain computationally demanding, whereas data-driven approaches im…

• Neuro-Symbolic Participation Governance for Verifiable AI Agents in Open Digital Twin Ecosystems

  Juan Li, Wei Cai, Yan Bai · 4 de agosto de 2026

  Autonomous AI agents, increasingly empowered by large language models, are becoming important components of human-machine systems for high-stakes decision support in digital twin ecosystems. However, existing multi-agent systems often lack robust verification for identity, capability, and policy com…

• HenTwin: A Multimodal Digital Twin Framework for Longitudinal Biological State Monitoring in Laying Hens

  Yashan Dhaliwal, Shreya Rao, Suresh Neethirajan · 3 de agosto de 2026

  Early-life monitoring in laying hens remains constrained by fragmented single-modality sensing and the absence of formal system-level state representations. HenTwin, a multimodal digital twin framework implemented as a five-layer IoT architecture, formalizes flock-level multimodal biological state d…

• Scoping Review of AI, Metrology, and ESG in the Semiconductor Sector: Implications for Safe and Sustainable by Design (SSbD)

  Karen Ang, Han-Teng Liao · 28 de julio de 2026

  The semiconductor sector faces a dual transition: scaling manufacturing execution through Artificial Intelligence (AI) while satisfying stringent sustainability mandates, such as the EU Carbon Border Adjustment Mechanism (CBAM). This paper presents a scoping review of 1,465 documents indexed in Web …

• Multi-Agent System-driven Digital Twins for predictive maintenance: architectures, technologies and open research challenges

  Korota Ars\`ene Coulibaly, Mohamed Hamlich · 27 de julio de 2026

  Digital twins have emerged as a foundational technology within the context of Industry 4.0, offering a paradigm for the real-time virtual representation of physical systems. However, managing their growing complexity, particularly in distributed industrial environments, requires intelligent architec…

• Design of policy digital twins incorporating multi-level agent based modelling

  Matt Tipuric, Alejandro Beltran, Nick Malleson, Dani Arribas-Bel, David Wagg · 16 de julio de 2026

  Digital twins are used across many industries to enable better decision making. However, while policy makers at all levels (including city, national and supranational scales) have expressed a desire to integrate digital twins into their workflows, this adoption has been slow to materialise. In this …

• A Conceptual Architecture for Educational Digital Twins Supporting AI Literacy Across Educational and Professional Settings

  Antonio Bucchiarone, Riccardo Belliato, Judith Michael, Michael Mittermaier · 14 de julio de 2026

  In the AI Literacy for Multidisciplinary Professional Readiness and Outreach (AIM-PRO) project, we are creating integrated methods to improve the education on AI literacy. One concept on which the project relies is educational digital twins, that is, digital representations of educator trainers, tea…

• $\text{DT}^2$: Decision-Targeted Digital Twins

  Harry Amad, Mihaela van der Schaar · 25 de junio de 2026

  A digital twin (DT) is a virtual model of a real-world system that can assist decision-making by simulating scenarios induced by different policies. However, typical machine learning-based DTs do not optimise for this use case. We prove that, when model capacity is limited, training DTs to minimise …