Accelerating gradient-based topology optimization design with dual-model neural networks

TL;DR

This paper introduces dual-model neural networks as surrogate models to replace finite element analysis in topology optimization, significantly reducing computational costs while maintaining high accuracy.

Contribution

It presents a novel dual-model neural network approach integrated with the SIMP method for faster topology optimization, with effective data generation strategies for training.

Findings

137x faster in forward calculations

74x faster in sensitivity analysis

95% design accuracy with 2000 training samples

Abstract

Topology optimization (TO) is a common technique used in free-form designs. However, conventional TO-based design approaches suffer from high computational cost due to the need for repetitive forward calculations and/or sensitivity analysis, which are typically done using high-dimensional simulations such as Finite Element Analysis (FEA). In this work, neural networks are used as efficient surrogate models for forward and sensitivity calculations in order to greatly accelerate the design process of topology optimization. To improve the accuracy of sensitivity analyses, dual-model neural networks that are trained with both forward and sensitivity data are constructed and are integrated into the Solid Isotropic Material with Penalization (SIMP) method to replace FEA. The performance of the accelerated SIMP method is demonstrated on two benchmark design problems namely minimum compliance design and metamaterial design. The efficiency gained in the problem with size of 64x64 is 137 times in forward calculation and 74 times in sensitivity analysis. In addition, effective data generation methods suitable for TO designs are investigated and developed, which lead to a great saving in training time. In both benchmark design problems, a design accuracy of 95% can be achieved with only around 2000 training data.