Ryan Hansen Lee

Title: Ryan Hansen Lee and Mechanical Neural Networks

Introduction

Ryan Hansen Lee is an inventor associated with the University of California in Los Angeles, California. His work includes a patent describing mechanical neural networks, an approach that combines architected materials with learning-like behavior.

Latest Patents

Lee is associated with 1 patent, titled “Mechanical Neural Networks: Architected Materials That Learn Behaviors.” The patent describes beams arranged in a lattice, with each beam designed to change stiffness in response to external loads. By tuning the stiffness of its constituent beams, the lattice can learn a set of properties through exposure to continuous loads. The design can be two- or three-dimensional, occupy volumes of arbitrary shape, and include multiple layers of nodes analogous to neurons in an artificial neural network.

Career Highlights

The patent highlights Lee’s work on mechanical systems that adapt their physical properties in response to loading. The proposed mechanical neural network offers a way to encode and adjust behavior through the structure and stiffness of its beams.

Collaborations

Lee’s listed coworker is Erwin Ab Mulder. The patent’s institutional association is the University of California.

Conclusion

Ryan Hansen Lee’s patent presents a mechanical neural-network concept in which a lattice learns by changing the stiffness of its beams, connecting adaptive materials with ideas from artificial neural networks.