arXiv 2511.137982025-11-17cs.AIcs.CVcs.LG

KANGURA：面向复杂结构三维建模的基于Kolmogorov-Arnold网络、具备统一表征注意力的几何感知学习方法

KANGURA: Kolmogorov-Arnold Network-Based Geometry-Aware Learning with Unified Representation Attention for 3D Modeling of Complex Structures

Department of Electrical Engineering, Iran University of Science and Technology（伊朗科学技术大学电气工程系）；Department of Computer Engineering, Iran University of Science and Technology（伊朗科学技术大学计算机工程系）；Department of Industrial & Management Systems Engineering, West Virginia University（西弗吉尼亚大学工业与管理系统工程系）；H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology（佐治亚理工学院H.米尔顿·斯图尔特工业与系统工程学院）；Department of Computer Science, University of California, Davis（加州大学戴维斯分校计算机科学系）

Mohammad Reza Shafie, Morteza Hajiabadi, Hamed Khosravi, Mobina Noori, Imtiaz Ahmed

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AI总结：

针对现有模型难以捕捉微生物燃料电池阳极等复杂结构几何依赖的问题，提出KANGURA三维建模框架，在基准和真实任务中均取得优于15种以上SOTA模型的准确率，可支撑复杂结构优化应用。

摘要：

微生物燃料电池（MFCs）可通过微生物过程将有机物转化为电能，为可持续能源生产提供了极具前景的实现路径。阳极结构是影响MFC性能的关键因素，其设计与材料特性起到核心作用。现有预测模型难以捕捉优化这类结构所需的复杂几何依赖关系。为解决该问题，我们提出KANGURA：基于Kolmogorov-Arnold网络、具备统一表征注意力的几何感知学习方法。KANGURA为三维（3D）机器学习建模提供了全新思路，它将预测任务建模为函数分解问题，其中基于Kolmogorov-Arnold网络（KAN）的表征学习无需传统多层感知器（MLP）即可重建几何关系。为优化空间理解能力，几何解耦表征学习将结构变化拆分为可解释组件，同时统一注意力机制可动态增强关键几何区域的特征。实验结果表明，KANGURA在ModelNet40基准数据集上的表现优于15种以上当前最优（SOTA）模型，准确率达92.7%；在真实场景的MFC阳极结构任务中表现出色，准确率达97%。这证明KANGURA是鲁棒的3D几何建模框架，为先进制造与质量导向工程应用中的复杂结构优化开辟了新可能。

英文摘要：

Microbial Fuel Cells (MFCs) offer a promising pathway for sustainable energy generation by converting organic matter into electricity through microbial processes. A key factor influencing MFC performance is the anode structure, where design and material properties play a crucial role. Existing predictive models struggle to capture the complex geometric dependencies necessary to optimize these structures. To solve this problem, we propose KANGURA: Kolmogorov-Arnold Network-Based Geometry-Aware Learning with Unified Representation Attention. KANGURA introduces a new approach to three-dimensional (3D) machine learning modeling. It formulates prediction as a function decomposition problem, where Kolmogorov-Arnold Network (KAN)- based representation learning reconstructs geometric relationships without a conventional multi- layer perceptron (MLP). To refine spatial understanding, geometry-disentangled representation learning separates structural variations into interpretable components, while unified attention mechanisms dynamically enhance critical geometric regions. Experimental results demonstrate that KANGURA outperforms over 15 state-of-the-art (SOTA) models on the ModelNet40 benchmark dataset, achieving 92.7% accuracy, and excels in a real-world MFC anode structure problem with 97% accuracy. This establishes KANGURA as a robust framework for 3D geometric modeling, unlocking new possibilities for optimizing complex structures in advanced manufacturing and quality-driven engineering applications.

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