Digital Twins

Follow this topic Ask about this topic

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.

Online Bayesian Calibration under Gradual and Abrupt System Changes
May 7, 2026Yang Xu, Chiwoo ParkParticle Filtering Bayesian Filtering
Department of Industrial and Systems Engineering University of Washington Seattle, WA 98195
Physics-based Digital Twins for Integrated Thermal Energy Systems Using Active Learning
May 7, 2026Umme Mahbuba Nabila, Paul Seurin, Linyu Lin +1Surrogate Modeling Sparse Identification of Nonlinear Dynamics
Department of Nuclear Engineering and Radiological Sciences, University of Michigan, Ann Arbor, MI 48109, United States · Nuclear Science & Technology Division, Idaho National Laboratory, Idaho Falls, ID 83415, United States · Department of Computer Science and Engineering, University of Michigan, Ann Arbor, MI 48109, United States
A Scalable Digital Twin Framework for Energy Optimization in Data Centers
May 7, 2026Raphael Hendrigo de Souza Gonçalves, Wendel Marcos dos SantosData Center Energy Management Digital Twins
Federal University of São João del-Rei (UFSJ) · Federal Institute of Education, Science and Technology of São Paulo (IFSP)
Housing Potential Common Data Model and City Digital Twin
May 7, 2026Megan Katsumi, Mark Fox, Anderson Wong +1Urban Planning Digital Twins
Urban Data Research Centre School of Cities University of Toronto
Behavior-Grounded Lane Representation Learning for Multi-Task Traffic Digital Twins
May 3, 2026Rei Tamaru, Pei Li, Bin RanRepresentation Learning Digital Twins
Department of Civil and Environmental Engineering, University of Wisconsin-Madison, Madison, WI, USA · Department of Civil and Architecture Engineering and Construction Management, University of Wyoming, Laramie, WY, USA
Equation-Free Digital Twins for Nonlinear Structural Dynamics
May 1, 2026Mohammad Mahdi Abaei, Ahmad BahooToroody, Arttu Polojärvi +4Koopman Operator Learning Nonlinear System Identification
Department of Mechanical Engineering, Marine Technology and Arctic Group, Aalto University, Finland · Independent Expert, Digital Twin Technology, Ribe, Denmark · Institute for Risk and Reliability, Leibniz University Hannover, Germany
Network Digital Untwinning: Towards Backward Optimization of Digital Twins
Apr 30, 2026Zifan Zhang, Dianwei Chen, Anjun Gao +5Digital Twins Machine Unlearning
North Carolina State University, USA · University of Maryland, USA · University of Louisville, USA +2
Toward Personalized Digital Twins for Cognitive Decline Assessment: A Multimodal, Uncertainty-Aware Framework
Apr 29, 2026Bulent Soykan, Gulsah Hancerliogullari Koksalmis, Hsin-Hsiung Huang +1Alzheimer's Disease Clinical Outcome Prediction
Department of Mechanical, Industrial & Manufac. Eng. The University of Toledo Toledo, OH, USA · Department of Industrial Eng. and Mngt. Systems University of Central Florida Orlando, FL, USA · Department of Statistics and Data Science University of Central Florida Orlando, FL, USA +1
Threat-Oriented Digital Twinning for Security Evaluation of Autonomous Platforms
Apr 28, 2026Thomas J. Neubert, Laxima Niure Kandel, Berker PeközAdversarial Robustness Cybersecurity
Department of Electrical Engineering and Computer Science Embry-Riddle Aeronautical University Daytona Beach, FL, USA
CognitiveTwin: Robust Multi-Modal Digital Twins for Predicting Cognitive Decline in Alzheimer's Disease
Apr 24, 2026Bulent Soykan, Gulsah Hancerliogullari Koksalmis, Hsin-Hsiung Huang +1Alzheimer's Disease Multimodal Robustness
Department of Mechanical, Industrial & Manufacturing Eng. The University of Toledo Toledo, OH, USA · Department of Industrial Eng. and Mngt. Systems University of Central Florida Orlando, FL, USA · Department of Statistics and Data Science University of Central Florida Orlando, FL, USA +1
Optimal sequential decision-making for error propagation mitigation in digital twins
Apr 24, 2026Annice Najafi, Shokoufeh MirzaeiMarkov Decision Processes Partially Observable Markov Decision Processes
Department of Industrial and Manufacturing Engineering California State Polytechnic University, Pomona Pomona, CA, 91768, USA
Generative Flow Networks for Model Adaptation in Digital Twins of Natural Systems
Apr 22, 2026Pascal Archambault, Houari Sahraoui, Eugene SyrianiSimulation-Based Inference Model Calibration
DIRO, Université de Montréal, Quebec, Canada.
Data-Driven Open-Loop Simulation for Digital-Twin Operator Decision Support in Wastewater Treatment
Apr 22, 2026Gary Simethy, Daniel Ortiz Arroyo, Petar DurdevicNeural Surrogate Modeling Digital Twins
Aalborg University, Department of Energy, Niels Bohrs Vej 8, Esbjerg, DK-6700, Denmark
Aether: Network Validation Using Agentic AI and Digital Twin
Apr 20, 2026Jordan Auge, Sam Betts, Giovanna Carofiglio +3Agentic Workflows Digital Twins
Cisco Systems · Cisco Systems, Paris, France and London, UK. · Swisscom +1
Public and private blockchain for decentralized digital building twins and building automation system
Apr 16, 2026Reachsak Ly, Alireza ShojaeiCybersecurity Digital Twins
Assistant Professor, School of Technology, Eastern Illinois University, Charleston, Illinois, United States · Assistant Professor, Myers-Lawson School of Construction, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States
Towards Trustworthy 6G Network Digital Twins: A Framework for Validating Counterfactual What-If Analysis in Edge Computing Resources
Apr 16, 2026Julian Jimenez Agudelo, Paola Soto, Ayat Zaki-Hindi +56G Networks Digital Twins
University of Antwerp - imec, IDLab, Belgium · Luxembourg Institute of Science and Technology (LIST), Luxembourg
From Brain Models to Executable Digital Twins: Execution Semantics and Neuro-Neuromorphic Systems
Apr 15, 2026Alexandre MuzyDigital Twins
ILLS · International Laboratory on Learning Systems (ILLS) CNRS – McGill – ETS – MILA – Université Paris-Saclay
GeoVision-Enabled Digital Twin for Hybrid Autonomous-Teleoperated Medical Responses
Apr 14, 2026Parham Kebria, Soheil Sabri, Laura J BrattainDigital Twins Robotics
Electrical and Computer Engineering / North Carolina A&T State University / Greensboro, USA · Urban Digital Twin Lab, School of Modeling Simulation and Training / University of Central Florida / Orlando, USA · Department of Internal Medicine / College of Medicine, University of Central Florida / Orlando, USA
Bridging the Gap between Micro-scale Traffic Simulation and 4D Digital Cityscapes
Apr 9, 2026Longxiang Jiao, Lukas Hofmann, Yiru Yang +2Virtual Reality Digital Twins
ETH Zurich · University of Zurich
Joint Task Offloading, Inference Optimization and UAV Trajectory Planning for Generative AI Empowered Intelligent Transportation Digital Twin
Apr 9, 2026Xiaohuan Li, Junchuan Fan, Bingqi Zhang +3Intelligent Transportation Systems Digital Twins
Guangxi University Key Laboratory of Intelligent Networking and Scenario System (School of Information and Communication, Guilin University of Electronic Technology), Guilin 541004, China · Research Institute of Highway Ministry of Transport, Beijing 100000, China · School of Automation, Guangdong University of Technology, Guangzhou 510006, China +1
Dual-Loop Control in DCVerse: Advancing Reliable Deployment of AI in Data Centers via Digital Twins
Apr 8, 2026Qingang Zhang, Yuejun Yan, Guangyu Wu +4RL Control Digital Twins
College of Computing and Data Science, Nanyang Technological University, 639798, Singapore · Alibaba Group, Hangzhou, 311121, China
SYN-DIGITS: A Synthetic Control Framework for Calibrated Digital Twin Simulation
Apr 8, 2026Grace Jiarui Fan, Chengpiao Huang, Tianyi Peng +2Synthetic Control Language Model Calibration
Finance Division, Columbia Business School. · Department of IEOR, Columbia University · Decision, Risk, and Operations Division, Columbia Business School. +1
Graph Neural ODE Digital Twins for Control-Oriented Reactor Thermal-Hydraulic Forecasting Under Partial Observability
Apr 8, 2026Akzhol Almukhametov, Doyeong Lim, Rui Hu +1Surrogate Modeling Digital Twins
Department of Nuclear Engineering, Texas A&M University, College Station, TX 77843, USA · Argonne National Laboratory, Nuclear Science and Engineering Division, USA
TwinLoop: Simulation-in-the-Loop Digital Twins for Online Multi-Agent Reinforcement Learning
Apr 8, 2026Nan Zhang, Zishuo Wang, Shuyu Huang +4Digital Twins Multi-Agent Reinforcement Learning
Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China · Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, China · Department of Informatics and Telecommunications, University of Thessaly, Lamia, Greece
Bridging Natural Language and Microgrid Dynamics: A Context-Aware Simulator and Dataset
Apr 7, 2026Tinko Sebastian Bartels, Ruixiang Wu, Xinyu Lu +5Power Systems Digital Twins
School of Data Science, The Chinese University of Hong Kong-Shenzhen, Shenzhen, China. · Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong, China.
2026 Roadmap on Artificial Intelligence and Machine Learning for Smart Manufacturing
Apr 5, 2026Jay Lee, Hanqi Su, Marco Macchi +51Smart Manufacturing Manufacturing
A physics-informed, behavior-aware digital twin for robust multimodal forecasting of core body temperature in precision livestock farming
Apr 5, 2026Riasad Alvi, Mohaimenul Azam Khan Raiaan, Sadia Sultana Chowa +6Precision Livestock Farming Time Series Forecasting
Applied Artificial INtelligence and Intelligent Systems (AAIINS) Laboratory, Dhaka 1217, Bangladesh · Department of Computer Science and Engineering, United International University, Dhaka 1212, Bangladesh · Department of Data Science and Artificial Intelligence, Monash University, Melbourne 3800, Australia +5
An Intelligent Fault Diagnosis Method for General Aviation Aircraft Based on Multi-Fidelity Digital Twin and FMEA Knowledge Enhancement
Apr 3, 2026Zhihuan Wei, Yang Hu, Xinhang Chen +3Fault Diagnosis Digital Twins
Hangzhou International Innovation Institute, Beihang University, Hangzhou, China · School of Mechanical Engineering, Zhejiang University, Hangzhou, China · School of Reliability and Systems Engineering, Beihang University, Beijing, China +1
Transformer self-attention encoder-decoder with multimodal deep learning for response time series forecasting and digital twin support in wind structural health monitoring
Apr 2, 2026Feiyu Zhou, Marios ImpraimakisStructural Health Monitoring Digital Twins
Department of Mechanical Engineering, University of Bath, BA2 7AY Bath, UK · Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China
The Digital Twin Counterfactual Framework: A Validation Architecture for Simulated Potential Outcomes
Apr 1, 2026Olav LaudyCounterfactual Evaluation Causal Effect Estimation

May 7, 2026J/K move · Enter open · S save

Online Bayesian Calibration under Gradual and Abrupt System Changes

Yang Xu, Chiwoo Park

Department of Industrial and Systems Engineering University of Washington Seattle, WA 98195

Particle Filtering Bayesian Filtering Model Calibration Digital Twins

Open paper AskPDF arXiv

Bayesian model calibration is central to digital twins and computer experiments, as it aligns model outputs with field observations by estimating calibration parameters and correcting systematic model bias. Classical Bayesian calibration introduces latent parameters and a discrepancy function to model bias, but suffers from parameter--discrepancy confounding and is typically formulated as an offline procedure under a stationary data-generating assumption. These limitations are restrictive in modern digital twin applications, where systems evolve over time and may exhibit gradual drift and abrupt regime shifts. While data assimilation methods enable sequential updates, they generally do not explicitly model systematic bias and are less effective under abrupt changes. We propose Bayesian Recursive Projected Calibration (BRPC), an online Bayesian calibration framework for streaming data under simulator mismatch and nonstationarity. BRPC extends projected calibration to the online setting by separating a discrepancy-free particle update for calibration parameters from a conditional Gaussian process update for discrepancy, preserving identifiability while enabling bias-aware adaptation under gradual system evolution. To handle abrupt changes, BRPC is integrated with restart mechanisms that detect regime shifts and reset the calibration process. We establish theoretical guarantees for both components, including tracking performance under gradual evolution and false-alarm and detection behavior for restart mechanisms. Empirical studies on synthetic and plant-simulation benchmarks show that BRPC improves calibration accuracy under gradual changes, while restart-augmented BRPC further improves robustness and predictive performance under abrupt regime shifts compared to sliding-window Bayesian calibration and data assimilation baselines.