arXiv Machine Learning

One-Shot Generative Design for Disordered Metamaterials via Self-Organizing Neural Cellular Automata

arXiv:2607. 14475v1 Announce Type: cross Abstract: Disordered metamaterials feature microstructures with inherent randomness and irregularity, enabling them to achieve broader property coverage and superior performance unavailable in their regular counterparts.

arXiv Machine Learning
Jul 21

Harnessing disorder to decouple extension and shear in kirigami metamaterials

arXiv:2607. 16583v1 Announce Type: cross Abstract: Kirigami turns stiff sheets into compliant, shape-morphing structures, but its reliance on periodic cut patterns comes at a cost: correlated panel rotations couple extension to shear, so stretching one axis drives a parasitic shear that cannot be suppressed, and also confine anisotropic stiffness to a narrow, discrete set of responses that cannot be tuned independently.

By Haomin Yu, Hanxun Jin, Mingxuan Bi, Mohammad Jafari, Feng Helen Long, Michael J Greenberg, Farid Alisafaei, Guy Genin
arXiv AI
Jul 29

Steering topology distributions for unified generative design of architected metamaterials

arXiv:2607. 24777v1 Announce Type: new Abstract: Architected metamaterials derive their functions from structure, creating vast opportunities to program physical responses through topology design.

By Haolin Li, Yuyang Miao, Menglei Li, Jinshuai Bai, Liyuan Wang, Xin Liu, Bo Gao, Jiantao Liu, Danilo Mandic, Zahra Sharif Khodaei, M. H. Aliabadi, Weiqiu Chen
arXiv AI
Jul 28

Physics-Guided Generative AI for Property-Targeted 3D Porous Media Design

arXiv:2607. 24274v1 Announce Type: cross Abstract: Inverse design of three-dimensional porous media is central to applications in filtration, catalysis, energy storage, fuel cells, thermal management, and biomedical scaffolds, but remains challenging because many distinct pore geometries can share similar porosity or permeability while small structural changes can strongly affect transport behaviour.

By Peng Wang
arXiv Machine Learning
Jul 7

CertMix: Certified, Data-Efficient Metamaterial Design by Affine Mixing of Aligned Neural-Implicit Weight Spaces

arXiv:2607. 04123v1 Announce Type: new Abstract: Inverse design of mechanical metamaterials seeks a periodic unit cell whose homogenized elastic properties meet a prescribed target, but current learning-based methods are data-hungry, mostly interpolative, and provide no guarantee that the generated design satisfies the specification.

By Yifan Wang
arXiv AI
Jul 10

Architecture Generalization with MetaNCA

arXiv:2607. 07743v1 Announce Type: cross Abstract: Self-organization is an emergent property of life, driven by the collective behavior of individual components acting on local information.

By Meet Barot, Daniel Berenberg, Sina Khajehabdollahi
arXiv AI
Aug 5

Self-Organising Digital Circuits

arXiv:2608. 02606v1 Announce Type: new Abstract: Fault tolerance in classical computing has traditionally relied on static strategies like hardware redundancy and error-correcting codes.

By Marcello Barylli, Gabriel B\'ena, Alexander Mordvintsev, Eleni Nisioti, Sebastian Risi
Hugging Face Trending Papers
Jul 8

Neural Operator-enabled Topology-informed Evolutionary Strategy for PDE-Constrained Optimization

The inverse design of physical systems governed by partial differential equations is computationally demanding due to the high dimensionality and non-convexity of design spaces. Generative models for inverse design often lack robustness and transferability, whereas evolutionary strategies are robust but struggle in high-dimensional spaces.