This article explores groundbreaking research by the University of Michigan on mechanical neural networks (MNNs), materials capable of learning and performing computations. By adapting the backpropagation algorithm used in digital neural networks, researchers trained physical lattices to respond to inputs like force and modify their properties to achieve desired outputs. These 3D-printed lattices, modeled after neural connections, adjust segment stiffness to solve tasks such as species identification or complex mechanical responses. This innovation hints at a future where materials, like airplane wings, could autonomously optimize themselves for changing conditions. The approach also offers insights into biological learning processes and paves the way for more advanced applications using materials like polymers and nanoparticle assemblies.
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