Results 61 to 70 of about 2,450 (178)

Machine Learning‐Assisted Design and Performance Prediction of a Compact Dual‐Band Polarization‐Insensitive THz Metamaterial Absorber for Skin‐Cancer‐Related Refractive‐Index Sensing

open access: yesAdvanced Electronic Materials, EarlyView.
A compact QASRR‐based THz metamaterial absorber enables polarization‐insensitive dual‐band absorption and skin‐cancer‐related refractive‐index sensing through measurable resonance shifts. Field, surface‐current, and circuit analyses clarify the dual‐resonance mechanism, while StackNet‐assisted prediction accurately estimates the simulated absorption ...
Md. Murad Kabir Nipun   +5 more
wiley   +1 more source

Low-frequency attenuation signal absorption performance of thin-film acoustic metamaterials

open access: yesActa Acustica
Considering that thin film acoustic metamaterials have many special properties that natural materials and traditional materials do not possess, the low-frequency attenuation signal absorption performance of thin film acoustic metamaterials is studied ...
Xu Jingcheng, Chen Changzheng
doaj   +1 more source

Deep Learning‐Assisted Design of Mechanical Metamaterials

open access: yesAdvanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong   +5 more
wiley   +1 more source

Topology optimization of acoustic metamaterials with negative mass density using a level set-based method

open access: yesMechanical Engineering Journal, 2014
Much effort has been made to experimentally fabricate acoustic metamaterials that display novel properties, such as single or double negativity, negative refractive index.
Lirong LU   +5 more
doaj   +1 more source

Evolution of Physical Intelligence Across Scales

open access: yesAdvanced Intelligent Discovery, EarlyView.
By following the evolution of physical intelligence across scales, this article shows how intelligence arises from materials, structures, physical interactions, and collectives. It establishes physical intelligence as the evolutionary foundation upon which embodied intelligence is built.
Ke Liu   +7 more
wiley   +1 more source

Theoretical Optimization of Trapped-Bubble-Based Acoustic Metamaterial Performance

open access: yesApplied Sciences, 2020
Acoustic metamaterials have proven to be a versatile tool for the precise control and manipulation of sound waves. One of the promising designs of acoustic metamaterials employ the arrays of bubbles and find applications for soundproofing, blast ...
Dmitry Gritsenko, Roberto Paoli
doaj   +1 more source

Machine Learning Driven Inverse Design of Broadband Acoustic Superscattering

open access: yesAdvanced Intelligent Discovery, EarlyView.
Multilayer acoustic superscatterers are designed using machine learning to achieve broadband superscattering and strong sound insulation. By incorporating a weighted mean absolute error into the loss function, the forward and inverse neural networks accurately map structural parameters to spectral responses.
Lijuan Fan, Xiangliang Zhang, Ying Wu
wiley   +1 more source

Two-Dimensional Pentamode Metamaterials: Properties, Manufacturing, and Applications

open access: yesCrystals
Metamaterials are artificial materials with properties depending mainly on their designed structures instead of their materials. Pentamode metamaterials are one type of metamaterial.
Chuang Zhou   +4 more
doaj   +1 more source

Fine manipulation of sound via lossy metamaterials with independent and arbitrary reflection amplitude and phase

open access: yesNature Communications, 2018
The formation of true holograms requires control of both amplitude and phase; however, acoustic metamaterials are generally limited to phase control only. Here, Zhu et al.
Yifan Zhu   +6 more
doaj   +1 more source

Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning

open access: yesAdvanced 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

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