A dual physics-informed neural network for topology optimization

TL;DR

This paper introduces a dual physics-informed neural network that integrates physics constraints with neural networks for efficient and accurate topology optimization in 2D and 3D, surpassing traditional methods.

Contribution

It presents a novel dual neural network framework that embeds physical laws into topology optimization, eliminating the need for large datasets and sensitivity analysis.

Findings

Outperforms conventional topology optimization methods

Efficiently handles complex 3D and multi-load scenarios

Produces high-resolution, accurate designs

Abstract

We propose a novel dual physics-informed neural network for topology optimization (DPNN-TO), which merges physics-informed neural networks (PINNs) with the traditional SIMP-based topology optimization (TO) algorithm. This approach leverages two interlinked neural networks-a displacement network and an implicit density network-connected through an energy-minimization-based loss function derived from the variational principles of the governing equations. By embedding deep learning within the physical constraints of the problem, DPNN-TO eliminates the need for large-scale data and analytical sensitivity analysis, addressing key limitations of traditional methods. The framework efficiently minimizes compliance through energy-based objectives while enforcing volume fraction constraints, producing high-resolution designs for both 2D and 3D optimization problems. Extensive numerical validation demonstrates that DPNN-TO outperforms conventional methods, solving complex structural optimization scenarios with greater flexibility and computational efficiency, while addressing challenges such as multiple load cases and three-dimensional problems without compromising accuracy.