Page 2: 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.

Energy & Green Tech

Improving power communication systems with knowledge graphing

New research published in the International Journal of Information and Communication Technology suggests that so-called knowledge graphs, a form of AI-based data organization, could improve the reliability and maintenance ...

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 ...

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