Results 281 to 290 of about 175,630 (355)
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 +12 more
wiley +1 more source
Surrogate model for statics of fractional thin bar element and its equivalence with mass-spring metamaterial. [PDF]
Szajek K, Sumelka W.
europepmc +1 more source
Deep Learning‐Assisted Design of Mechanical Metamaterials
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
Deep Learning-Based Inverse Design of Stochastic-Topology Metamaterials for Radar Cross Section Reduction. [PDF]
Zhang C, Zou C, Guo S, Zhao Y, Shen T.
europepmc +1 more source
Re-Entrant Chiral Origami Metamaterials with Superior Compressive Stability
Haiying Yang, Haibao Lu, Yongqing Fu
openalex +1 more source
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 +3 more
wiley +1 more source
Bio-inspired material-structure-function integrated additive manufacturing of Al-based metamaterials with surpassing energy absorption. [PDF]
He X +16 more
europepmc +1 more source

