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Recent advances in implicit neural representations show great promise when itcomes to generating numerical solutions to partial differential equations.Compared to conventional alternatives, such representations employparameterized neural networks to define, in a mesh-free manner, signals thatare highly-detailed, continuous, and fully differentiable. In this work, wepresent a novel machine learning approach for topology optimization -- animportant class of inverse problems with high-dimensional parameter spaces andhighly nonlinear objective landscapes. To effectively leverage neuralrepresentations in the context of mesh-free topology optimization, we usemultilayer perceptrons to parameterize both density and displacement fields.Our experiments indicate that our method is highly competitive for minimizingstructural compliance objectives, and it enables self-supervised learning ofcontinuous solution spaces for topology optimization problems.

NTopo: Mesh-free Topology Optimization using Implicit Neural Representations