这篇论文用物理信息学习解决张拉整体结构的形式发现难题，能同时预测构型和内力，比传统方法更鲁棒。
本文提出一个能量基学习框架，用于簇状张拉整体结构的形式发现和物理属性预测。该方法将总势能最小化与构型关系纳入训练目标，同时预测平衡节点构型和杆件力、力密度等物理量。数值实验在棱柱和着陆器系统上验证了框架的可扩展性和准确预测能力。该框架提升了物理一致性、鲁棒性和数据效率。
Energy-Based Physics-Informed Form Finding for Clustered Tensegrity Structures
Tensegrity form-finding and physical property prediction are fundamental inverse 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, the need to ensure structural stability, and the enforcement of constraints such as boundary conditions and symmetry. Moreover, traditional methods often lack robustness to noise and outliers. This paper proposes an energy-based learning framework 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 simultaneous prediction of equilibrium nodal configurations and associated physical quantities, including member forces and force densities. By incorporating energy-based physical losses directly into the learning process, the framework improves physical consistency, robustness, and data efficiency. Numerical experiments on tensegrity structures, including prism and lander systems, show the great potential of the proposed approach and demonstrate its capability for scalable form finding and accurate prediction of structural properties.