Real-Time Topology Optimization in 3D via Deep Transfer Learning

TL;DR

This paper presents a transfer learning approach using CNNs for real-time 3D topology optimization, significantly reducing computational costs and enabling dynamic design exploration with high accuracy.

Contribution

The authors introduce a novel transfer learning method that enables real-time 3D topology optimization across various shapes and boundary conditions, with high efficiency and accuracy.

Findings

Achieved around 95% binary accuracy in predictions.

Demonstrated effectiveness on unseen 3D domains.

Enabled real-time design exploration with reduced training data.

Abstract

The published literature on topology optimization has exploded over the last two decades to include methods that use shape and topological derivatives or evolutionary algorithms formulated on various geometric representations and parametrizations. One of the key challenges of all these methods is the massive computational cost associated with 3D topology optimization problems. We introduce a transfer learning method based on a convolutional neural network that (1) can handle high-resolution 3D design domains of various shapes and topologies; (2) supports real-time design space explorations as the domain and boundary conditions change; (3) requires a much smaller set of high-resolution examples for the improvement of learning in a new task compared to traditional deep learning networks; (4) is multiple orders of magnitude more efficient than the established gradient-based methods, such as SIMP. We provide numerous 2D and 3D examples to showcase the effectiveness and accuracy of our proposed approach, including for design domains that are unseen to our source network, as well as the generalization capabilities of the transfer learning-based approach. Our experiments achieved an average binary accuracy of around 95% at real-time prediction rates. These properties, in turn, suggest that the proposed transfer-learning method may serve as the first practical underlying framework for real-time 3D design exploration based on topology optimization