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AI Material Learns Behaviors, Adapts to Changing Conditions

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A mechanical neural network.

The researchers plan to simplify the mechanical neural network's design so thousands of them can be manufactured on the micro-scale within 3D lattices for practical material applications.

Credit: Flexible Research Group/UCLA

University of California, Los Angeles mechanical engineers have created a new class of material that can learn behaviors over time and develop a "muscle memory" of its own, allowing for real-time adaptation to changing external forces, much like a pianist who learns to play their instrument without looking at the keys or a basketball player who puts in countless hours to throw a seemingly effortless jump shot.

The material is constructed of a structural system with tunable beams that allows it to change its shape and behaviors in response to dynamic circumstances. The study's findings, which have implications in the building of buildings, the development of aircraft, and imaging technologies among others, were recently published in the journal Science Robotics.

“This research introduces and demonstrates an artificial intelligent material that can learn to exhibit the desired behaviors and properties upon increased exposure to ambient conditions,” said mechanical and aerospace engineering professor Jonathan Hopkins of the UCLA Samueli School of Engineering who led the research. “The same foundational principles that are used in machine learning are used to give this material its smart and adaptive properties.”

From SciTechDaily
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