A Dual Physics-Informed Kolmogorov-Arnold Neural Network Framework for Continuum Topology Optimization

TL;DR

This paper introduces a novel dual neural network framework using physics-informed Kolmogorov-Arnold networks to improve continuum topology optimization by enhancing accuracy and reducing computational costs.

Contribution

The study develops a dual-HRKAN-based method that incorporates learnable activation functions for efficient and adaptable PDE solution and sensitivity analysis in topology optimization.

Findings

DPIKAN-TO outperforms traditional PINN methods in computational efficiency.

Successfully optimized material layouts for various complex systems.

Framework easily extends to new PDEs due to learnable activation functions.

Abstract

In continuum topology optimization (TO), two essential procedures are involved: structural analysis through the solution of partial differential equations (PDEs) and the subsequent update of design variables. Both procedures can be addressed by training neural networks using the corresponding physical information. Accordingly, Physics-Informed Neural Network (PINN)-based algorithms have been developed for TO. However, PINN-based methods suffer from several notable limitations, including high computational cost, spectral bias, and limited adaptability in solving PDEs.To overcome these challenges, this study proposes a novel algorithm that incorporates two Higher-Order ReLU-based Kolmogorov-Arnold Networks (HRKANs). Specifically, a displacement-informed HRKAN (d-HRKAN) is designed to predict PDE solutions, while a sensitivity-informed HRKAN (s-HRKAN) is developed to perform sensitivity analysis for updating design variables. For convenience, the proposed approach is referred to as the Dual Physics-Informed Kolmogorov-Arnold Networks-based Topology Optimization (DPIKAN-TO) method. By leveraging learnable activation functions, the proposed neural networks can accurately approximate the responses of complex structural systems. Moreover, compared with conventional PINN-based methods, DPIKAN-TO demonstrates significantly improved computational efficiency and reduced computational cost. Numerical examples show that DPIKAN-TO can successfully identify optimal material layouts for linear structures, compliant mechanisms, and fluid-solid coupled systems. Furthermore, owing to the use of learnable activation functions, the proposed framework can be readily extended to structural optimization problems governed by new types of PDEs.