Results 151 to 160 of about 14,234 (266)

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

Toward Knowledge‐Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human–AI Synergy

Advanced Intelligent Discovery, EarlyView.
This perspective highlights how knowledge‐guided artificial intelligence can address key challenges in manufacturing inverse design, including high‐dimensional search spaces, limited data, and process constraints. It focused on three complementary pillars—expert‐guided problem definition, physics‐informed machine learning, and large language model ...
Hugon Lee, Hyeonbin Moon, Junhyeong Lee, Seunghwa Ryu   +3 more
wiley   +1 more source

Design of Quasi-Zero-Stiffness Metamaterials Featuring Adjustable Thermal Expansion. [PDF]

Materials (Basel)
Li Z, Zhang L, He Z, Wang H, Ding Z, Wang H, Pei Y.   +6 more
europepmc   +1 more source

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

3D self-locking granular metamaterials. [PDF]

Sci Adv
Liu T, Sun T, Cao N, Shen Y, Wang K, Long H, Guo Y, Zhang Y, Li X, Wu Y.   +9 more
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

Author Correction: Acoustic metamaterials-driven transdermal drug delivery for rapid and on-demand management of acute disease. [PDF]

Nat Commun
Xu J, Cai H, Wu Z, Li X, Tian C, Ao Z, Niu VC, Xiao X, Jiang L, Khodoun M, Rothenberg M, Mackie K, Chen J, Lee LP, Guo F.   +14 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

Numerical analysis of dispersion and elastic wave propagation in spatiotemporally modulated spring-mass metamaterials. [PDF]

Sci Rep
Liao SC, Ko CC, Chang IL.
europepmc   +1 more source

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