Results 181 to 190 of about 2,660 (259)
Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning
This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley +1 more source
ESICM LIVES 2024. Barcelona, Spain. 5–9 October 2024. [PDF]
europepmc +1 more source
Hiroki SHIMIZU +3 more
openaire +2 more sources
Harnessing Machine Learning to Understand and Design Disordered Solids
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley +1 more source
An electromagnetic manipulation system enhances magnetic field strength in the Z‐direction for 3D control of microrobots and nanoparticles. Featuring eight metal‐core coils and two air‐core coils arranged hemispherically, it ensures unimpeded workspace access and integrates imaging tools.
Nader Latifi Gharamaleki +4 more
wiley +1 more source
This article describes a multimodal fusion data acquisition and processing system about electromyography for dynamic movement recognition and bioelectrical impedance for key posture recognition. In addition, a new dynamic–static fusion algorithm strategy is designed.
Chenhao Cao +5 more
wiley +1 more source
Commentary: an eye on PET quantification. [PDF]
Walker MD, Sossi V.
europepmc +1 more source

