基于能量的聚类张拉整体结构物理信息找形
Energy-Based Physics-Informed Form Finding for Clustered Tensegrity Structures
- University of Kentucky（肯塔基大学）
- University of Houston（休斯顿大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对聚类张拉整体结构找形及物理特性预测难题，提出基于能量的学习框架，将总势能最小化和本构关系纳入训练目标以同时预测相关物理量，经实验验证该方法有潜力且能准确预测结构特性。
AI中文摘要：
张拉整体找形和物理特性预测是结构力学中的基本反问题，旨在确定平衡构型和内力分布。由于几何与力耦合产生的强非线性、确保结构稳定性以及施加边界条件和对称性等约束，这些问题具有挑战性。此外，传统方法对噪声和异常值缺乏鲁棒性。本文提出了一个基于能量的学习框架用于聚类张拉整体找形和物理特性预测。该方法将总势能最小化和本构关系纳入训练目标，能同时预测平衡节点构型和相关物理量。通过将基于能量的物理损失直接纳入学习过程，提高了物理一致性、鲁棒性和数据效率。在包括棱柱和着陆器系统的张拉整体结构上的数值实验表明了该方法的巨大潜力。
英文摘要：
Tensegrity form-finding and physical property prediction are fundamental problems in structural mechanics, which aim to determine equilibrium configurations and internal force distributions. These problems are challenging due to strong nonlinearity arising from the coupling between geometry and forces, and the need to satisfy equilibrium, stability, and structural constraints. This paper proposes an energy-based learning approach for clustered tensegrity form finding and physical property prediction. The proposed approach incorporates total potential energy minimization and constitutive relations into the training objective, enabling the prediction of equilibrium nodal configurations and the reconstruction of physical quantities such as member forces and force densities. By integrating energy-based physical losses directly into the learning process, the method promotes physical consistency while combining data-driven learning with physics-based constraints. Numerical experiments on tensegrity prism and lander structures demonstrate accurate prediction of equilibrium configurations and internal forces across different training-data ratios, indicating the potential of the proposed approach for nonlinear tensegrity form finding and structural analysis.
