NTopo: Mesh-free Topology Optimization using Implicit Neural Representations

Understand

Recent advances in implicit neural representations show great promise when it comes to generating numerical solutions to partial differential equations.

• Compared to conventional alternatives, such representations employ parameterized neural networks to define, in a mesh-free manner, signals that are highly-detailed, continuous, and fully differentiable.
• In this work, we present a novel machine learning approach for topology optimization -- an important class of inverse problems with high-dimensional parameter spaces and highly nonlinear objective landscapes.
• To effectively leverage neural representations in the context of mesh-free topology optimization, we use multilayer perceptrons to parameterize both density and displacement fields.