It’s easy to think that machine learning is a completely digital phenomenon, made possible by computers and algorithms that can mimic brain-like behaviors.

But the first machines were analog and now, a small but growing body of research is showing that mechanical systems are capable of learning, too. Physicists at the University of Michigan have provided the latest entry into that field of work.

The U-M team of Shuaifeng Li and Xiaoming Mao devised an algorithm that provides a mathematical framework for how learning works in lattices called mechanical neural networks.

“We’re seeing that materials can learn tasks by themselves and do computation,” Li said.

The researchers have shown how that algorithm can be used to “train” materials to solve problems, such as identifying different species of iris plants. One day, these materials could create structures capable of solving even more advanced problems—such as airplane wings that optimize their shape for different wind conditions—without humans or computers stepping in to help.

That future is a ways off, but insights from U-M’s new research could also provide more immediate inspiration for researchers outside the field, said Li, a postdoctoral researcher.

The algorithm is based on an approach called backpropagation, which has been used to enable learning in both digital and optical systems. Because of the algorithm’s apparent indifference to how information is carried, it could also help open new avenues of exploration into how living systems learn, the researchers said.

“We’re seeing the success of backpropagation theory in many physical systems,” Li said. “I think this might also help biologists understand how biological neural networks in humans and other species work.”

Please continue reading Matt Davenport's article on the Michigan News website.

More Information:

Professor Xiaoming Mao

Shuaifeng Li, Research Fellow

Study: Training all-mechanical neural networks for task learning through in situ backpropagation (DOI:10.1038/s41467-024-54849-z)