Research news on AI-enabled digital twins

AI-enabled digital twins combine high-fidelity virtual replicas of physical assets with machine learning and sensing technologies to monitor condition, predict failures, and support operational decision-making. Applications span bridges, railways, nuclear reactors, wind turbines, manufacturing equipment, and urban infrastructure, integrating structural health monitoring, non-destructive evaluation, and high-resolution imaging. Data-driven models enable real-time fault diagnosis, risk-informed maintenance, and optimization of performance, often incorporating robotics, remote sensing, and time-series domain adaptation for robust, continuous infrastructure management.

Robotics

Closing the gap between animal movement and robotic control

Animals move with a level of precision and adaptability that robots struggle to match. In Carnegie Mellon University's Department of Mechanical Engineering, researchers are developing a new AI-driven approach to uncover how ...

Engineering

Cooling without pumps: New measurement data for modular reactors

Passive cooling systems for nuclear power plants operate without pumps or electricity: They rely solely on physical effects such as density differences to dissipate heat. Researchers at the Paul Scherrer Institute PSI have ...

Engineering

Automated camera solution can improve excavator tracking

Despite significant advances in vision-based equipment tracking, frequent occlusions caused by multiple interacting machines continue to degrade tracking accuracy on construction sites. While previous studies have explored ...

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