We introduce JAX FDM, a differentiable solver to design mechanically efficient shapes for 3D structures conditioned on target architectural, fabrication and structural properties. Examples of such structures are domes, cable nets and towers. JAX FDM solves these inverse form-finding problems by combining the force density method, differentiable sparsity and gradient-based optimization. Our solver can be paired with other libraries in the JAX ecosystem to facilitate the integration of form-finding simulations with neural networks. We showcase the features of JAX FDM with two design examples. JAX FDM is available as an open-source library at https://github.com/arpastrana/jax_fdm.

翻译：我们提出JAX FDM——一种可微分的求解器，用于设计以目标建筑、制造和结构特性为条件的3D结构力学高效形状。此类结构的示例包括穹顶、索网和塔架。JAX FDM通过结合力密度法、可微稀疏性与基于梯度的优化，解决了这些反向形态寻优问题。该求解器可与JAX生态系统中的其他库协同使用，从而促进形态寻优模拟与神经网络的集成。我们通过两个设计案例展示了JAX FDM的功能。JAX FDM以开源库形式发布于https://github.com/arpastrana/jax_fdm。