Accurately Models the Relationship Between Physical Response and Structure Using Kolmogorov–Arnold Network
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...
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| Format: | Article |
| Language: | English |
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Wiley
2025-03-01
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| Series: | Advanced Science |
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| Online Access: | https://doi.org/10.1002/advs.202413805 |
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| author | Yang Wang Changliang Zhu Shuzhe Zhang Changsheng Xiang Zhibin Gao Guimei Zhu Jun Sun Xiangdong Ding Baowen Li Xiangying Shen |
| author_facet | Yang Wang Changliang Zhu Shuzhe Zhang Changsheng Xiang Zhibin Gao Guimei Zhu Jun Sun Xiangdong Ding Baowen Li Xiangying Shen |
| author_sort | Yang Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e421b2b9b9544207b77ab5c81d4e599d |
| institution | OA Journals |
| issn | 2198-3844 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Science |
| spelling | doaj-art-e421b2b9b9544207b77ab5c81d4e599d2025-08-20T02:10:46ZengWileyAdvanced Science2198-38442025-03-011212n/an/a10.1002/advs.202413805Accurately Models the Relationship Between Physical Response and Structure Using Kolmogorov–Arnold NetworkYang Wang0Changliang Zhu1Shuzhe Zhang2Changsheng Xiang3Zhibin Gao4Guimei Zhu5Jun Sun6Xiangdong Ding7Baowen Li8Xiangying Shen9State Key Laboratory for Mechanical Behavior of Materials School of Materials Science and Engineering Xi'an Jiaotong University Xi'an 710049 P. R. ChinaDepartment of Physics Southern University of Science and Technology Shenzhen 518055 P. R. ChinaDepartment of Materials Science and Engineering Southern University of Science and Technology Shenzhen 518055 P. R. ChinaState Key Laboratory for Mechanical Behavior of Materials School of Materials Science and Engineering Xi'an Jiaotong University Xi'an 710049 P. R. ChinaState Key Laboratory for Mechanical Behavior of Materials School of Materials Science and Engineering Xi'an Jiaotong University Xi'an 710049 P. R. ChinaSchool of Microelectronics Southern University of Science and Technology Shenzhen 518055 P. R. ChinaState Key Laboratory for Mechanical Behavior of Materials School of Materials Science and Engineering Xi'an Jiaotong University Xi'an 710049 P. R. ChinaState Key Laboratory for Mechanical Behavior of Materials School of Materials Science and Engineering Xi'an Jiaotong University Xi'an 710049 P. R. ChinaDepartment of Physics Southern University of Science and Technology Shenzhen 518055 P. R. ChinaDepartment of Physics Southern University of Science and Technology Shenzhen 518055 P. R. ChinaAbstract 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.https://doi.org/10.1002/advs.202413805Kolmogorov‐Arnold NetworkPoisson's ratioMechanical propertyDeep learning |
| spellingShingle | Yang Wang Changliang Zhu Shuzhe Zhang Changsheng Xiang Zhibin Gao Guimei Zhu Jun Sun Xiangdong Ding Baowen Li Xiangying Shen Accurately Models the Relationship Between Physical Response and Structure Using Kolmogorov–Arnold Network Advanced Science Kolmogorov‐Arnold Network Poisson's ratio Mechanical property Deep learning |
| title | Accurately Models the Relationship Between Physical Response and Structure Using Kolmogorov–Arnold Network |
| title_full | Accurately Models the Relationship Between Physical Response and Structure Using Kolmogorov–Arnold Network |
| title_fullStr | Accurately Models the Relationship Between Physical Response and Structure Using Kolmogorov–Arnold Network |
| title_full_unstemmed | Accurately Models the Relationship Between Physical Response and Structure Using Kolmogorov–Arnold Network |
| title_short | Accurately Models the Relationship Between Physical Response and Structure Using Kolmogorov–Arnold Network |
| title_sort | accurately models the relationship between physical response and structure using kolmogorov arnold network |
| topic | Kolmogorov‐Arnold Network Poisson's ratio Mechanical property Deep learning |
| url | https://doi.org/10.1002/advs.202413805 |
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