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.

Engineering

Using AI models to detect sinkhole trouble

Researchers at the University of Florida are developing artificial intelligence models to pinpoint early signs of sinkholes before they appear. "I'm always looking for real-world problems," said Minhee Kim, Ph.D., an assistant ...

Energy & Green Tech

Researchers measure traffic emissions, to the block, in real-time

In a study focused on New York City, MIT researchers have shown that existing sensors and mobile data can be used to generate a near real-time, high-resolution picture of auto emissions, which could be used to develop local ...

Engineering

Diffusion-based AI model successfully trained in electroplating

Electrochemical deposition, or electroplating, is a common industrial technique that coats materials to improve corrosion resistance and protection, durability and hardness, conductivity and more. A Los Alamos National Laboratory ...

Engineering

AI-based model measures atomic defects in materials

In biology, defects are generally bad. But in materials science, defects can be intentionally tuned to give materials useful new properties. Today, atomic-scale defects are carefully introduced during the manufacturing process ...

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