8 citations · 8 across the 6 of their papers we have counts for
6 papers
In situ local learning of dynamic network materials
Shuaifeng Li, Xiaoming Mao
Functional materials realize functionalities through pre-designed structures, whereas physical neural networks are trained by prescribing input-output behavior. Unifying these view…
Topological mechanical neural networks as classifiers through in situ backpropagation learning
Shuaifeng Li, Xiaoming Mao
Recently, a new frontier in computing has emerged with physical neural networks(PNNs) harnessing intrinsic physical processes for learning. Here, we explore topological mechanical…
Training all-mechanical neural networks for task learning through in situ backpropagation
Shuaifeng Li, Xiaoming Mao
Recent advances unveiled physical neural networks as promising machine learning platforms, offering faster and more energy-efficient information processing. Compared with extensive…
Nanoscale Imaging of Phonons and Reconfiguration in Topologically-Engineered, Self-Assembled Nanoparticle Lattice
Chang Qian, Ethan Stanifer, Zhan Ma +6
Topologically-engineered mechanical frames are important model constructs for architecture, machine mechanisms, and metamaterials. Despite significant advances in macroscopically f…
Universal spectral moment theorem and its applications in non-Hermitian systems
Nan Cheng, Chang Shu, Kai Zhang +2
The high sensitivity of the spectrum and wavefunctions to boundary conditions, termed the non-Hermitian skin effect, represents a fundamental aspect of non-Hermitian systems. While…
Topological pumping in origami metamaterials
Shuaifeng Li, Panayotis G. Kevrekidis, Xiaoming Mao +1
In this study, we present a mechanism of topological pumping in origami metamaterials with spatial modulation by tuning the rotation angles. Through coupling spatially modulated or…