ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025
Accurately Models the Relationship Between Physical Response and Structure Using Kolmogorov-Arnold Network.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- Modeling the interpretable geometric-performance relationship of metamaterials on small datasets using Kolmogorov-Arnold operator informed network.Scientific reports · 2026Article
- Highly Efficient Discovery of 3D Mechanical Metamaterials via Monte Carlo Tree Search.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Accurately Models the Relationship Between Physical Response and Structure Using Kolmogorov-Arnold Network.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Artificial intelligence (AI) in science is a key area of modern research. However, many current machine learning methods lack interpretability, making it difficult to grasp the physical mechanisms behind various phenomena, which hampers progress in related fields. This study focuses on the Poisson's ratio of a hexagonal lattice elastic network as it varies with structural deformation. By employing the Kolmogorov-Arnold Network (KAN), the transition of the network's Poisson's ratio from positive to negative as the hexagonal structural element shifts from a convex polygon to a concave polygon was accurately predicted. The KAN provides a clear mathematical framework that describes this transition, revealing the connection between the Poisson's ratio and the geometric properties of the hexagonal element, and accurately identifying the geometric parameters at which the Poisson's ratio equals zero. This work demonstrates the significant potential of the KAN network to clarify the mathematical relationships that underpin physical responses and structural behaviors.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.