Results 201 to 210 of about 5,042 (302)

CrossMatAgent: AI‐Assisted Design of Manufacturable Metamaterial Patterns via Multi‐Agent Generative Framework

Advanced Intelligent Discovery, EarlyView.
CrossMatAgent is a multi‐agent framework that combines large language models and diffusion‐based generative AI to automate metamaterial design. By coordinating task‐specific agents—such as describer, architect, and builder—it transforms user‐provided image prompts into high‐fidelity, printable lattice patterns.
Jie Tian, Martin Taylor Sobczak, Dhanush Patil, Jixin Hou, Lin Pang, Arunachalam Ramanathan, Libin Yang, Xianyan Chen, Yuval Golan, Xiaoming Zhai, Hongyue Sun, Kenan Song, Xianqiao Wang   +12 more
wiley   +1 more source

Inverse Design of Ultrathin Metamaterial Absorber. [PDF]

Nanomaterials (Basel)
Jang E, Cho J, Kang C, Chung H.
europepmc   +1 more source

Computational Electromagnetics [PDF]

, 2003
Monk, Peter, Carstensen, Carsten, Funken, Stefan, Hackbusch, Wolfgang, Hoppe, Ronald H. W.   +4 more
openaire   +2 more sources

Singular High-order Complete Vector Bases for the Finite Element Modeling of Wedge Problems [PDF]

, 2004
GRAGLIA, Roberto, Graglia R.D, Lombardi, Guido   +2 more
core  

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced 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, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +1 more source

Regularization of electromagnetic scattering problems via the Abel integral transform. [PDF]

Philos Trans A Math Phys Eng Sci
Vinogradova E, Smith P.
europepmc   +1 more source

Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

Advanced Intelligent Discovery, EarlyView.
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang, Xinyi Chen, Xu Ye, Yulin Yang, Bin Ai   +4 more
wiley   +1 more source

Broadband Achromatic Programmable Electromagnetic Camouflage via Fluidic-Accessible Metasurface. [PDF]

Adv Sci (Weinh)
Liu S, Zhang J, Wang J, Chen Z, Yu Z, Luo J, Luo J, Cao R, Gong S, Guo Q, Li P, Li R, Chen K, Jia Y, Liu Y.   +14 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

Manipulation of ultrafast nonlinear optical response based on plasmon-induced magnetic anapole mode. [PDF]

RSC Adv
He Y, Wang J, Yang W, Jiang S, Zhuo L.
europepmc   +1 more source