University of California, Los Angeles engineers have developed a new class of material. 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 c
Photo of a mechanical neural network . Currently, the system is about the size of a microwave oven, but the researchers plan to simplify the MNN design so that thousands of the networks can be manufactured on the micro-scale within 3D lattices for practical material applications. Credit: Flexible Research Group at UCLAUniversity of California, Los Angeles
A short video of a mechanical neural network learning to adjust its rigidity in response to applied forces. Credit: Flexible Research Group at UCLA An optimization algorithm then regulates the entire system by taking the data from each of the strain gauges and determining a combination of rigidity values to control how the network should adapt to applied forces.
“Identifying the reasons why [the networks] failed to learn is important for understanding how to design MNNs that successfully learn,” the researchers shared how they solved the problem through trial and error over the past five years.
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