Nature evolves structures like honeycombs at optimized performance with limited material. These efficient structures can be artificially created with the collaboration of structural topology optimization and additive manufacturing. However, the extensive computation cost of topology optimization causes low mesh resolution, long solving time, and rough boundaries that fail to match the requirements for meeting the growing personal fabrication demands and printing capability. Therefore, we propose the neural synthesizing topology optimization that leverages a self-supervised coordinate-based network to optimize structures with significantly shorter computation time, where the network encodes the structural material layout as an implicit function of coordinates. Continuous solution space is further generated from optimization tasks under varying boundary conditions or constraints for users' instant inference of novel solutions. We demonstrate the system's efficacy for a broad usage scenario through numerical experiments and 3D printing.

翻译：自然界通过有限的材料演化出如蜂窝般性能最优的结构。这些高效结构可通过结构拓扑优化与增材制造的协同作用人工生成。然而，拓扑优化带来的高计算成本导致网格分辨率低、求解时间长、边界粗糙，难以满足日益增长的个人制造需求与打印能力要求。为此，我们提出神经合成拓扑优化方法，利用基于自监督坐标的网络显著缩短结构优化计算时间——该网络将结构材料布局编码为坐标的隐函数。进一步地，通过在不同边界条件或约束下的优化任务生成连续解空间，支持用户即时推理新型解决方案。通过数值实验与3D打印，我们验证了该系统在广泛使用场景中的有效性。