Towards Domain-Adaptive, Resolution-Free 3D Topology Optimization With Neural Implicit Fields

Abstract Topology optimization is a ubiquitous task in engineering design, involving the optimal distribution of material in a prescribed spatial domain. Recently, data-driven methods such as deep generative AI models have been proposed as an alternative to iterative optimization methods. However, existing data-driven approaches are often trained on datasets using fixed grid resolutions and domain shapes, reducing their applicability to different resolutions or different domain shapes. In this paper, we introduce two key innovations — a fast TO solver and a neural implicit field architecture to address these limitations. First, we introduce a fast, parallelizable, iterative GPU-based TO solver optimized for high-throughput dataset generation for 3D unstructured meshes. Our solver generated 122K optimized 3D topologies, an order of magnitude more than the largest existing public dataset. Second, we introduce a new resolution-free data-driven method for 3D topologies using neural fields, called NITO-3D. A single NITO-3D model trains and predicts for a variety of resolutions and aspect ratios. By also eliminating the need for computationally intensive physical field conditioning, NITO-3D offers a faster, more flexible alternative for 3D topology optimization. On average, NITO-3D generates topologies roughly 2000 times faster and with only 0.3% higher compliance than state-of-the-art iterative solvers. With 10 steps of iterative fine-tuning, NITO-3D is on average 15 times faster and generates topologies that are under 0.1% more compliant than SIMP’s. We open-source all data and code associated with this work.