  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.
