Scalable Deep-Learning-Accelerated Topology Optimization for Additively Manufactured Materials

TL;DR

This paper introduces SDL-TO, a scalable deep learning framework that accelerates topology optimization for additively manufactured materials by leveraging parallel high-performance computing, reducing computational costs significantly.

Contribution

The paper presents a novel DL-based topology optimization framework that learns iterative history and gradients, enabling fast online updates and high scalability for industrial applications.

Findings

Achieves approximately 8.6x speedup over standard TO methods.

Demonstrates effectiveness on heat conduction and AM materials design.

Reduces computational cost while maintaining competitive performance.

Abstract

Topology optimization (TO) is a popular and powerful computational approach for designing novel structures, materials, and devices. Two computational challenges have limited the applicability of TO to a variety of industrial applications. First, a TO problem often involves a large number of design variables to guarantee sufficient expressive power. Second, many TO problems require a large number of expensive physical model simulations, and those simulations cannot be parallelized. To address these issues, we propose a general scalable deep-learning (DL) based TO framework, referred to as SDL-TO, which utilizes parallel schemes in high performance computing (HPC) to accelerate the TO process for designing additively manufactured (AM) materials. Unlike the existing studies of DL for TO, our framework accelerates TO by learning the iterative history data and simultaneously training on the mapping between the given design and its gradient. The surrogate gradient is learned by utilizing parallel computing on multiple CPUs incorporated with a distributed DL training on multiple GPUs. The learned TO gradient enables a fast online update scheme instead of an expensive update based on the physical simulator or solver. Using a local sampling strategy, we achieve to reduce the intrinsic high dimensionality of the design space and improve the training accuracy and the scalability of the SDL-TO framework. The method is demonstrated by benchmark examples and AM materials design for heat conduction. The proposed SDL-TO framework shows competitive performance compared to the baseline methods but significantly reduces the computational cost by a speed up of around 8.6x over the standard TO implementation.