Digital Twins

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Digital twins are computational counterparts of physical assets, people, or broader systems, updated with observations to represent their changing state and test possible actions. Approaches range from physics-based simulation and data assimilation to machine-learning surrogates, hybrid models, generative agents, and reinforcement learning for control. Applications span manufacturing, infrastructure, transportation, healthcare, energy, and social systems, where twins can support forecasting, maintenance, safety testing, and personalized decisions. A central challenge is ensuring that a twin remains calibrated and trustworthy as conditions change, while balancing fidelity, uncertainty, computation, privacy, and validation against real-world outcomes.

Latest papers

255 papers36 papers in the last four weeks, up 16% on the four weeks before. 0.2% of all new papers.

In-Vehicle Digital Twin-Based Collision Warning Framework with Sybil Attack Detection
Jun 26, 2026Mohammad Imtiaz Hasan, Abyad Enan, Jean Michel Tine +3Digital Twins
Glenn Department of Civil Engineering, Clemson University, Clemson, 29634, South Carolina, USA
SimFoundry: Modular and Automated Scene Generation for Policy Learning and Evaluation
Jun 26, 2026Nadun Ranawaka, Josiah Wong, Wei-Lin Pai +15Robotics Simulation Sim-to-Real Transfer
NVIDIA · Stanford University · The University of Texas at Austin +2
Language-Based Digital Twins for Elderly Cognitive Assistance
Jun 25, 2026Mohammad Mehdi Hosseini, Mohammad H. Mahoor, Hiroko H. DodgeDigital Twins Clinical NLP
Ritchie School of Engineering and Computer Science, University of Denver, Denver, CO 80208, USA · Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
$\text{DT}^2$ : Decision-Targeted Digital Twins
Jun 24, 2026Harry Amad, Mihaela van der SchaarDecision-Focused Learning Digital Twins
Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom.
Neural operator-based digital twins for modeling amyloid- $β$ and tau propagation and treatment optimization in Alzheimer's disease
Jun 23, 2026Xiaofeng Xu, Tingting Dan, Zifan Zhou +3Alzheimer's Disease PDE Operator Learning
Department of Mathematics, Pennsylvania State University, University Park, PA 16802, USA · Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA · School of Electrical Engineering and Computer Science, Pennsylvania State University, University Park, PA 16802, USA +3
ArtiTwinSplat: Interactable Digital Twin Reconstruction via Gaussian Splatting from RGB-D videos
Jun 23, 2026Pranjal Mishra, René Zurbrügg, Max Wilder-Smith +4Articulated Object Reconstruction 3D Gaussian Splatting
1ETH Zürich, Zürich, Switzerland. · 2Microsoft, Zürich, Switzerland. · University of Bonn, Bonn, Germany.
Digital Twin-Driven Adaptive Sim-to-Real Alignment via Reinforcement Learning for Vibration-Based Bearing Health Monitoring Under Data Scarcity
Jun 23, 2026Jinghan Wang, Yanjun Chen, Wei Zhang +3Synthetic-to-Real Domain Adaptation Bearing Fault Diagnosis
a Harbin Institute of Technology, China · b Eastern Institute of Technology, China
Sat2City v2: Native 3D City Asset Generation from a Single Satellite Image
Jun 23, 2026Tongyan Hua, Dongli Wu, Jinjing Zhu +53D Mesh Generation 3D Asset Generation
The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China · The Chinese University of Hong Kong, Hong Kong SAR, China · Auckland University of Technology, Auckland, New Zealand +1
Towards a Bathroom-Centered Human-Building Digital Twin Framework for Indoor Safety Analysis
Jun 22, 2026Yuanzhi Su, Cynthia HouDigital Twins Human-Object Interaction
aDepartment of Building Environment and Energy Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Cognitive Digital Twins: Ethical Risks and Governance for AI Systems That Model the Mind
Jun 22, 2026Vamshi Krishna Bonagiri, Juan Nicolas Sepulveda-Arias, Abdoul Jalil Djiberou Mahamadou +1AI Accountability Digital Twins
Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)
A Query-Driven Communication-Efficient Digital Twins Design for Autonomous Driving
Jun 22, 2026Nuocheng Yang, Longyu Zhou, Sihua Wang +2Autonomous Driving Digital Twins
Beijing Laboratory of Advanced Information Network, and the Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing 100876, China · Information Systems Technology and Design Pillar, Singapore University of Technology and Design, 487372, Singapore
OphthaDT: Generative Digital Twins for Forecasting Visual Acuity Trajectories in Ophthalmology
Jun 20, 2026Pietro Belligoli, Nikita Makarov, Sayedali Shetab Boushehri +3Clinical Outcome Prediction LLM-Based Time Series Forecasting
Computational Sciences Center of Excellence, Roche, Penzberg, Germany · Technical University of Munich, Munich, Germany · Computational Health Center, Helmholtz Munich, Munich, Germany +3
Federated Temporal Attention Intelligence for Cyber-Resilient IoMT: Lightweight Digital Twins and PPO-Driven Honeypot Deception
Jun 19, 2026Syed Zeeshan Haider, Anwar Shah, Muneeb Arif +2Federated RL Autonomous Cyber Defense
FAST National University of Computer and Emerging Sciences, Islamabad, Pakistan
Integrating Large Language Model Agents with Digital Twins for Industrial Autonomous Systems
Jun 18, 2026Yuchen XiaLanguage Model-Based Control Digital Twins
Institut für Automatisierungs- und Softwaresysteme (IAS) der Universität Stuttgart
A Digital Twin Framework for Traffic-Aware UAV Pavement Monitoring in Open-Traffic Conditions
Jun 17, 2026Yamil Uchani, Grace Luna, Edwin Salcedo +1UAV Navigation Digital Twins
Department of Mechatronics Engineering, Universidad Cat´olica Boliviana “San Pablo”, Bolivia · School of Electronic Engineering and Computer Science, Queen Mary University of London, United Kingdom
URDF Synthesis from RGB-D Sequences via Differentiable Joint Inference and Energy-Consistent Verification
Jun 17, 2026Xinze ZhangArticulated Object Reconstruction 3D Reconstruction
University of Southern California, Los Angeles, CA 90007, USA
Learning Cardiac Electrophysiology Digital Twins Through Agentic Discovery of Hybrid Structure
Jun 16, 2026Ziqi Zhou, Yubo Ye, Sumeet Atul Vadhavka +2Scientific Discovery Digital Twins
Rochester Institute of Technology, Rochester, NY, 14623, USA
FacProcessTwin: An LLM-Based System for Process Twin Development
Jun 16, 2026Yash Pulse, Yong-Bin Kang, Abhik Banerjee +1Manufacturing Human-in-the-Loop AI
Swinburne University of Technology Melbourne, Australia
Treatment Response Optimized Clinical Decision Support AI System via Digital Twin Simulation
Jun 16, 2026Xinyu Qin, Anil K. Sood, Ruiheng Yu +3Clinical Decision Support Constrained RL
Department of Biomedical Engineering, University of Houston, USA · Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, USA · Department of Health Systems & Population Health Sciences, University of Houston, USA
Training LLMs with Reinforcement Learning over Digital Twin Representations for Reasoning-Intensive Surgical VideoQA
Jun 15, 2026Yiqing Shen, Han Zhang, Mathias UnberathRL for Language Model Reasoning Medical VQA
Johns Hopkins University, Baltimore, MD, USA
Automated Digital Twin Construction for Highway Scenarios Using LiDAR Point Clouds and OpenStreetMap
Jun 15, 2026Yongqi Zhao, Dong Bi, Paul Kovacevic +4Digital Twins Autonomous Driving Simulation
Institute of Automotive Engineering, Graz University of Technology, 8010 Graz, Austria · School of Intelligent Connected Vehicle, Hubei University of Automotive Technology, 442002 Shiyan, China · 3Professorship of Autonomous Vehicle Systems, Technical University of Munich, 85748 Garching, Germany
Posterior Twins: Distributional Behavioral Simulation for Enterprise Decisions
Jun 15, 2026Ankit DasHuman Behavior Simulation Digital Twins
Twinning Labs · Twinning Labs, Inc.
Graphical conditional generative modeling for digital twin modeling
Jun 15, 2026Zongren Zou, Théo Bourdais, Ricardo Baptista +1Surrogate Modeling Digital Twins
Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA · Department of Statistical Sciences, University of Toronto, Toronto, Canada
EgoPhys: Learning Generalizable Physics Models of Deformable Objects from Egocentric Video
Jun 15, 2026Hyunjin Kim, Ri-Zhao Qiu, Guangqi Jiang +1Deformable Object Manipulation Deformable Shape Reconstruction
UC San Diego
HAPI-EP: Towards Hybrid, Adaptive, and Predictive Digital Twins of Cardiac Electrophysiology
Jun 14, 2026Sumeet Vadhavkar, Xiajun Jiang, Yubo Ye +2Adaptive Inference Physics-Informed ML
TwinBI: An Agentic Digital Twin for Efficient Augmented Interactions with Business Intelligence Dashboards
Jun 11, 2026Jisoo Jang Wen-Syan LiAI-Assisted Decision Making LLM Agents
Graduate School of Data Science, Seoul National University
Transformer-Guided Graph Attention for Direct Cardiac Mesh Reconstruction: A Structural Digital Twin Framework
Jun 11, 2026Abhishek H S, Akash Ganamukhi, Abhimanyu Suresh +33D Mesh Generation 3D Reconstruction
CAVE Labs, C-IoT, Dept. of CSE, PES University · C-IoT, Dept. of CSE, PES University
OmniBioTwin: A System-of-Twinned-Systems Framework for Health Digital Twins
Jun 9, 2026Zhaohui Wang, Yu Huang, Jiang BianDigital Twins
Indiana University School of Medicine Regenstrief Institute Indianapolis, Indiana, USA
Transition-Based Digital Twin Modelling for Alzheimer's Disease under Sparse Longitudinal Data
Jun 8, 2026Yinyu Huang, Yilin Zhang, Sofia Michopoulou +2Irregular Time-Series Modeling Clinical Outcome Prediction
School of Electronics and Computer Science, University of Southampton, UK · University Hospital Southampton NHS Foundation Trust, Southampton, UK · Faculty of Medicine, University of Southampton, UK
Autonomous heterogeneous catalyst discovery with a self-evolving multi-agent digital twin
Jun 3, 2026Zhilong Song, Zongmin Zhang, Lixue ChengMaterials Science Digital Twins
Department of Chemistry, Hong Kong University of Science and Technology, Kowloon, Hong Kong 999077, China · IAS Center for AI for Scientific Discoveries, Hong Kong University of Science and Technology, Kowloon, Hong Kong 999077, China · Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Kowloon, Hong Kong 999077, China +1

Jun 26, 2026J/K move · Enter open · S save

In-Vehicle Digital Twin-Based Collision Warning Framework with Sybil Attack Detection

Mohammad Imtiaz Hasan, Abyad Enan, Jean Michel Tine, Araf Rahman+2

Glenn Department of Civil Engineering, Clemson University, Clemson, 29634, South Carolina, USA

Digital Twins

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Connected Vehicles (CVs) rely extensively on communication technologies to enable data-driven predictive analyses for enhancing performance and safety. These communication channels can be exploited by adversaries to launch cyberattacks such as Sybil attacks, which could threaten both safety-critical and mobility applications, leaving CVs vulnerable and putting human lives at risk. As CV deployment continues to expand, the need to detect and mitigate cyberattacks in real-time becomes increasingly urgent. This study presents an in-vehicle Digital Twin (DT)-based collision warning framework with built-in capabilities for Sybil attacks detection. The framework integrates a Temporal Convolutional Network (TCN) for learning temporal dependencies in vehicle trajectory data and Hierarchical Navigable Small World (HNSW) algorithms for efficient similarity-based classification. Our framework is evaluated on real-world Sybil attack data, collected through field experiments. The framework achieved accuracy, recall, and F1 scores of 0.984, 1.00, and 0.944, respectively, in detecting Sybil-generated fake vehicles. During the safety evaluation, the framework reduced the mean Time Exposed Time-To-Collision (TET) and mean Time Integrated Time-To-Collision (TIT) of near-collision events by 88% and 72%, respectively. Furthermore, real-world feasibility evaluation shows that the framework conformed to the standardized maximum allowable latency for safety applications and operated well within the capacity of modern processors -- demonstrating the promise of an in-vehicle DT-based framework as an attack mitigation mechanism against Sybil attacks for next-generation CVs.