NTopo: Mesh-free Topology Optimization using Implicit Neural RepresentationsDownload PDF

21 May 2021, 20:44 (modified: 26 Oct 2021, 19:57)NeurIPS 2021 PosterReaders: Everyone
Keywords: implicit representations, topology optimization, bilevel optimization, multilayer perceptrons, mesh-free
TL;DR: We present a mesh-free topology optimization method using implicit neural representations, that also applies to learning solution spaces.
Abstract: 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. Our experiments indicate that our method is highly competitive for minimizing structural compliance objectives, and it enables self-supervised learning of continuous solution spaces for topology optimization problems.
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Code: https://github.com/JonasZehn/ntopo/
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