Results 201 to 210 of about 75,153 (270)

Design framework for programmable three-dimensional woven metamaterials. [PDF]

Nat Commun
Carton M, Surjadi JU, Aymon BFG, Xu L, Portela CM.   +4 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

Influence of Nodal Spheres on the Mechanical Behaviour of Auxetic Materials Manufactured with PA12. [PDF]

Materials (Basel)
Lamas I, Feijoo I, Gómez S, Pereira A, Pérez JA, Pérez MC.   +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

Correction for Keogh and Bilal, Combinatorial asymmetric acoustic metamaterials with real-time programmability. [PDF]

Proc Natl Acad Sci U S A
europepmc   +1 more source

Self-supervised AI for decoding and designing disordered metamaterials. [PDF]

Sci Adv
Shen M, Liu K, Mao S, Daraio C.
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

Quasi-3D Plasmonic Metamaterials with Highly Stretch-Tunable Optical Responses. [PDF]

ACS Appl Mater Interfaces
Chen IC, Huang YC, Chao WT, Kao YL, Lin YL, Liao KL.   +5 more
europepmc   +1 more source

Factorization Machine with Iterative Quantum Reverse Annealing: A Python Package for Batch Black‐Box Optimization With Reverse Quantum Annealing

Advanced Intelligent Discovery, EarlyView.
Factorization machine with iterative quantum reverse annealing (FMIRA) leverages quantum reverse annealing to perform batch black‐box optimization. Factorization machine with quantum annealing (FMQA) is a widely used python package for solving black‐box optimization problems using D‐Wave quantum annealers.
Andrejs Tučs, Ryo Tamura, Koji Tsuda
wiley   +1 more source

Robust and Integrable Time-Varying Metamaterials: A Systematic Survey and Coherent Mapping. [PDF]

Nanomaterials (Basel)
Koutzoglou I, Amanatiadis S, Kantartzis NV.   +2 more
europepmc   +1 more source