Results 211 to 220 of about 12,301 (257)
Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning
Advanced Intelligent Discovery, EarlyView.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
Harnessing Machine Learning to Understand and Design Disordered Solids
Advanced Intelligent Discovery, EarlyView.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
Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.Herein, a patient‐mounted neuro optical coherence tomography system that integrates a 5 degrees‐of‐freedom skull‐mounted robot (Skullbot) with a 0.6 mm neuroendoscope for targeted, minimally invasive deep brain imaging, is developed. The system offers high‐resolution imaging with precise deployment, demonstrated through successful tumor imaging in a ...
Chao Xu +7 more
wiley +1 more source
Predicting Performance of Hall Effect Ion Source Using Machine Learning
Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.This study introduces HallNN, a machine learning tool for predicting Hall effect ion source performance using a neural network ensemble trained on data generated from numerical simulations. HallNN provides faster and more accurate predictions than numerical methods and traditional scaling laws, making it valuable for designing and optimizing Hall ...
Jaehong Park +8 more
wiley +1 more source
Roadmap on Artificial Intelligence‐Augmented Additive Manufacturing
Advanced Intelligent Systems, EarlyView.This Roadmap outlines the transformative role of artificial intelligence‐augmented additive manufacturing, highlighting advances in design, monitoring, and product development. By integrating tools such as generative design, computer vision, digital twins, and closed‐loop control, it presents pathways toward smart, scalable, and autonomous additive ...
Ali Zolfagharian +37 more
wiley +1 more source
Non-Symbolic Magnitude Processing Is a Strong Correlate of Symbolic Math Skills in Children From Ghana and Côte d'Ivoire. [PDF]
Dev SciBugden S +4 more
europepmc +1 more source
physically interpretable residual strength prediction of corroded pipelines via symbolic Bayesian networks. [PDF]
Sci RepChen M, Zhang Y, Ye Y, Lu Y.
europepmc +1 more source
Dissociation of Size and Distance Effect in Numerical Magnitude Comparison in Less Familiar Number Ranges. [PDF]
J CognGarsmeur A, Morand R, Knops A.
europepmc +1 more source
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Symbolic regression via neural networks
16th World Congress on Computational Mechanics and 4th Pan American Congress on Computational Mechanics, 2023Identifying governing equations for a dynamical system is a topic of critical interest across an array of disciplines, from mathematics to engineering to biology. Machine learning—specifically deep learning—techniques have shown their capabilities in approximating dynamics from data, but a shortcoming of traditional deep learning is that there is ...
N. Boddupalli, T. Matchen, J. Moehlis
openaire +3 more sources