Generative Topology Optimization: Exploring Diverse Solutions in Structural Design

2025-02-17

AI Summary

Traditional topology optimization yields only a single optimal design, hindering exploratory alternatives. This work proposes Generative Topology Optimization (GenTO), a solver-in-the-loop neural network framework that directly synthesizes diverse, mechanically feasible structural configurations. Its core contribution is the first data-free, explicitly diversity-regularized generative paradigm for topology optimization, integrating (i) solver-in-the-loop differentiable training, (ii) implicit shape representation, (iii) differentiable structural compliance modeling, and (iv) parallelized diversity regularization. Evaluated on canonical 2D and 3D benchmark problems, GenTO achieves significantly higher solution diversity than state-of-the-art methods—while maintaining near-optimal mechanical performance—and accelerates inference by an order of magnitude.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Solvers and Tools
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Topology optimization (TO) is a family of computational methods that derive near-optimal geometries from formal problem descriptions. Despite their success, established TO methods are limited to generating single solutions, restricting the exploration of alternative designs. To address this limitation, we introduce Generative Topology Optimization (GenTO) - a data-free method that trains a neural network to generate structurally compliant shapes and explores diverse solutions through an explicit diversity constraint. The network is trained with a solver-in-the-loop, optimizing the material distribution in each iteration. The trained model produces diverse shapes that closely adhere to the design requirements. We validate GenTO on 2D and 3D TO problems. Our results demonstrate that GenTO produces more diverse solutions than any prior method while maintaining near-optimality and being an order of magnitude faster due to inherent parallelism. These findings open new avenues for engineering and design, offering enhanced flexibility and innovation in structural optimization.

Problem

• Generates diverse structural design solutions
• Overcomes single-solution limitation in topology optimization
• Ensures near-optimality with increased computational efficiency

Innovation

• Generative Topology Optimization method
• Neural network with diversity constraint
• Solver-in-the-loop training approach