Results 181 to 190 of about 33,091 (311)

Observation of synthetic moving effect in metamaterials. [PDF]

Light Sci Appl
Yang Q, Li Z, Wen X, You O, Ishihara T, Yin X, Zhang S.   +6 more
europepmc   +1 more source

Characterization of Metamaterials using a New Design and Measurement Technique for Microstrip Circuit Applications

, 2014
Characterization of a metamaterial based on an S-Shaped resonator and a feeding transmission line using a new measurement technique is studied in this paper. First, the metamaterial is designed and simulated using conventional methods.
Nesimoglu, Tayfun, Sabah, Cumali
core  

Factorization Machine‐Based Active Learning for Functional Materials Design with Optimal Initial Data

Advanced Intelligent Discovery, EarlyView.
This work investigates the optimal initial data size for surrogate‐based active learning in functional material optimization. Using factorization machine (FM)‐based quadratic unconstrained binary optimization (QUBO) surrogates and averaged piecewise linear regression, we show that adequate initial data accelerates convergence, enhances efficiency, and ...
Seongmin Kim, In‐Saeng Suh
wiley   +1 more source

Design of a 5G millimeter-wave wideband high-gain metamaterial-based antenna. [PDF]

Sci Rep
Osama I, Elhefnawy M, Yahya A.
europepmc   +1 more source

Evolution of Physical Intelligence Across Scales

Advanced 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, Tianyun Huang, Aming Li, Pengyu Lv, Tingting Qin, Wenhui Chen, Hui Wei, Huiling Duan   +7 more
wiley   +1 more source

Ultrathin S-Band Multifunctional Metamaterial with Broadband Microwave Absorption and Hydrophobic Characteristics. [PDF]

Nanomaterials (Basel)
Jin H, Pang H, Guan R, Yin S, An W, Wang C.   +5 more
europepmc   +1 more source

Machine Learning Driven Inverse Design of Broadband Acoustic Superscattering

Advanced 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

A Comparative Sound Intensity Method for Measuring the Increase in Sound Insulation of Small Acoustic Metamaterial Samples. [PDF]

Sensors (Basel)
Agata P, Katarzyna BM, Bartłomiej Z, Mirosława LM, Dorota M, Katarzyna D.   +5 more
europepmc   +1 more source

Plasmonic toroidal metamaterials at optical frequencies

, 2012
Toroidal dipole is created by currents flowing on a surface of a doughnut-shaped structure along its meridians first considered by Zel'dovich in 1957 [1]. Toroidal metamaterials were first theoretically proposed in 2007 [2].
Ho, You Zhe, Chau, Yuan-Fong, Chen, Wei Ting, Wu, Pin Chieh, Huang, Yao-Wei, Tsai, Din Ping, Zheludev, Nikolay I.   +6 more
core  

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